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EDUCAUSE QuickPoll Results: Did ChatGPT Write This Report?

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Generative AI is arriving in higher education, but stakeholders are uncertain about its potential opportunities and challenges. One thing is clear: th

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Google's introduction of Bard

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We’re launching a pilot subscription plan for ChatGPT, a conversational AI that can chat with you, answer follow-up questions, and challenge incorrect assumptions.

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Generate a ready-to-play lesson in seconds

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AI makes planning super fast and creative. Enter your grade and topic, and it will generate your inquiry based lesson in seconds!

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Futurepedia - Find The Best AI Tools & Software

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Futurepedia is a free site to help you find the best AI tools and software to make your work and life more efficient and productive. Updated daily, join millions of followers of our website, newsletter and YouTube.

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To see the full schema and try examples yourself, check out our API documentation.

Our classifier returns a document-level score completely_generated_prob that specifies the probability the entire document was AI-generated. We would recommend using this score when deciding whether or not there is a significant use of AI in generating the text.n our validation dataset, here is how the results change when you set all documents with completely_generated_prob under the threshold as human, and above as AI:

At a threshold of 0.88, 85% of AI documents are classified as AI, and 99% of human documents are classified as human

At a threshold of 0.5, 96% of AI documents are classified as AI, and 96% of human documents are classified as human

We recommend using a threshold of 0.88 or higher to minimize the number of false positives, as we think it is currently more harmful to falsely detect human writing as AI than vice versa.

Additionally, we highlight sentences that been detected to be written by AI. API users can access this highlighting through the highlight_sentence_for_ai field. The sentence-level classification should not be soley used to indicate that an essay contains AI (such as ChatGPT plagiarism). Rather, when a document gets a MIXED or AI_ONLY classification, the highlighted sentence will indicate where in the document we believe this occurred.

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GPTZero | The Trusted AI Detector for ChatGPT, GPT-4, & More

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Covered by >100 media outlets, GPTZero is the most advanced AI detector for ChatGPT, GPT-4, Bard. Check up to 50000 characters for AI plagiarism in seconds.

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"classifier correctly identifies 26% of AI-written text (true positives) as “likely AI-written,” while incorrectly labeling human-written text as AI-written 9% of the time (false positives)" -- the tool isn't fantastic, yet...

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SAN FRANCISCO (AP) - The maker of ChatGPT is trying to curb its reputation as a freewheeling cheating machine with a new tool that can help teachers detect if a student or artificial intelligence wrote that homework.

The new AI Text Classifier launched Tuesday by OpenAI follows a weeks-long discussion at schools and colleges over fears that ChatGPT's ability to write just about anything on command could fuel academic dishonesty and hinder learning.

OpenAI cautions that its new tool - like others already available - is not foolproof. The method for detecting AI-written text "is imperfect and it will be wrong sometimes," said Jan Leike, head of OpenAI's alignment team tasked to make its systems safer.

"Because of that, it shouldn't be solely relied upon when making decisions," Leike said.

Teenagers and college students were among the millions of people who began experimenting with ChatGPT after it launched Nov. 30 as a free application on OpenAI's website. And while many found ways to use it creatively and harmlessly, the ease with which it could answer take-home test questions and assist with other assignments sparked a panic among some educators.

By the time schools opened for the new year, New York City, Los Angeles and other big public school districts began to block its use in classrooms and on school devices.

The Seattle Public Schools district initially blocked ChatGPT on all school devices in December but then opened access to educators who want to use it as a teaching tool, said Tim Robinson, the district spokesman.

"We can't afford to ignore it," Robinson said.

The district is also discussing possibly expanding the use of ChatGPT into classrooms to let teachers use it to train students to be better critical thinkers and to let students use the application as a "personal tutor" or to help generate new ideas when working on an assignment, Robinson said.

School districts around the country say they are seeing the conversation around ChatGPT evolve quickly.

"The initial reaction was 'OMG, how are we going to stem the tide of all the cheating that will happen with ChatGPT,'" said Devin Page, a technology specialist with the Calvert County Public School District in Maryland. Now there is a growing realization that "this is the future" and blocking it is not the solution, he said.

"I think we would be naïve if we were not aware of the dangers this tool poses, but we also would fail to serve our students if we ban them and us from using it for all its potential power," said Page, who thinks districts like his own will eventually unblock ChatGPT, especially once the company's detection service is in place.

OpenAI emphasized the limitations of its detection tool in a blog post Tuesday, but said that in addition to deterring plagiarism, it could help to detect automated disinformation campaigns and other misuse of AI to mimic humans.

The longer a passage of text, the better the tool is at detecting if an AI or human wrote something. Type in any text -- a college admissions essay, or a literary analysis of Ralph Ellison's "Invisible Man" --- and the tool will label it as either "very unlikely, unlikely, unclear if it is, possibly, or likely" AI-generated.

But much like ChatGPT itself, which was trained on a huge trove of digitized books, newspapers and online writings but often confidently spits out falsehoods or nonsense, it's not easy to interpret how it came up with a result.

"We don't fundamentally know what kind of pattern it pays attention to, or how it works internally," Leike said. "There's really not much we could say at this point about how the classifier actually works."

Higher education institutions around the world also have begun debating responsible use of AI technology. Sciences Po, one of France's most prestigious universities, prohibited its use last week and warned that anyone found surreptitiously using ChatGPT and other AI tools to produce written or oral work could be banned from Sciences Po and other institutions.

In response to the backlash, OpenAI said it has been working for several weeks to craft new guidelines to help educators.

"Like many other technologies, it may be that one district decides that it's inappropriate for use in their classrooms," said OpenAI policy researcher Lama Ahmad. "We don't really push them one way or another. We just want to give them the information that they need to be able to make the right decisions for them."

It's an unusually public role for the research-oriented San Francisco startup, now backed by billions of dollars in investment from its partner Microsoft and facing growing interest from the public and governments.

France's digital economy minister Jean-Noël Barrot recently met in California with OpenAI executives, including CEO Sam Altman, and a week later told an audience at the World Economic Forum in Davos, Switzerland that he was optimistic about the technology. But the government minister - a former professor at the Massachusetts Institute of Technology and the French business school HEC in Paris - said there are also difficult ethical questions that will need to be addressed.

"So if you're in the law faculty, there is room for concern because obviously ChatGPT, among other tools, will be able to deliver exams that are relatively impressive," he said. "If you are in the economics faculty, then you're fine because ChatGPT will have a hard time finding or delivering something that is expected when you are in a graduate-level economics faculty."

He said it will be increasingly important for users to understand the basics of how these systems work so they know what biases might exist.

--

O'Brien reported from Providence, Rhode Island. AP writer John Leicester contributed to this report from Paris.

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Cheaters beware: ChatGPT maker releases AI detection tool

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SAN FRANCISCO (AP) - The maker of ChatGPT is trying to curb its reputation as a freewheeling cheating machine with a new tool that can help teachers detect if a student or artificial intelligence wrote that homework.

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OpenAI's Own New Detection Tool

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“We are going through a period of transition that always requires a period of adjustment,” said Giada Pistilli, principal ethicist at AI company Hugging Face. “

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Steph Swanson's latest cover letter begins like this: "I am writing to beg for the opportunity to apply for the position of professional dog food consumer in the abandoned parking garage."

The rest of the letter - which you can read here if you've got a strong stomach - only gets darker as the applicant expounds on her desire to stuff herself with pet food in a secluded parking complex.
Tech is not your friend. We are. Sign up for The Tech Friend newsletter.
It's disturbing. But Swanson isn't entirely responsible. The words were generated by the AI natural language model ChatGPT, with Swanson feeding it prompts and suggestions.

Swanson, who goes by the name "Supercomposite" online, is one of the artists and thinkers testing the possibilities of generative AI, or systems that spit out text or images in response to human input. During the past year, this technology went mainstream, with image generator DALL-E grabbing headlines and, most recently, a publicly available conversational bot built with the advanced language model GPT-3. This bot, named ChatGPT, can respond to questions and requests with the ease of an instant messenger. Its creator, OpenAI, made it available to the public in November, and a million people flocked to try it, the company says. (The site got so many visitors it has limited its traffic, OpenAI representatives said.)

The internet exploded with speculation on all the ways ChatGPT could make our lives easier, from writing work emails to brainstorming novels to keeping elderly people company. But generative AI's potential comes with giant liabilities, AI experts warn.

"We are going through a period of transition that always requires a period of adjustment," said Giada Pistilli, principal ethicist at AI company Hugging Face. "I am only disappointed to see how we are confronted with these changes in a brutal way, without social support and proper education."

Already, publications have put out AI-authored stories without clear disclosures. Mental health app Koko faced backlash after it used GPT-3 to help answer messages from people seeking mental health support. A Koko representative said the company takes the accusations seriously and is open to a "larger dialogue."

Tools like ChatGPT can be used for good or ill, Pistilli said. Often, companies and researchers will decide when and how it's deployed. But generative AI plays a role in our personal lives, as well. ChatGPT can write Christmas cards, breakup texts and eulogies - when is it okay to let the bot take the reins?

Help Desk asked the experts the best ways to experiment with ChatGPT during its early days. To try it, visit OpenAI's website.

For brainstorming, not truth-seeking

ChatGPT learned to re-create human language by scraping masses of data from the internet. And people on the internet are often mean or wrong - or both.

Never trust the model to spit out a correct answer, said Rowan Curran, a machine learning analyst at market research firm Forrester. Curran said that large language models like ChatGPT are notorious for issuing "coherent nonsense" - language that sounds authoritative but is actually babble. If you pass along its output without a fact check, you could end up sharing something incorrect or offensive.

Right now, the fastest way to fact check ChatGPT's output is to Google the same question and consult a reputable source - which you could have done in the first place. So it behooves you to stick to what the model does best: Generate ideas.

"When you are going for quantity over quality, it tends to be pretty good," said May Habib, of AI writing company Writer.

Ask ChatGPT to brainstorm captions, strategies or lists, she suggested. The model is sensitive to small changes in your prompt, so try specifying different audiences, intents and tones of voice. You can even provide reference material, she said, like asking the bot to write an invitation to a pool party in the style of a Victoria's Secret swimwear ad. (Be careful with that one.)

Text-to-image models like DALL-E work for visual brainstorms, as well, noted Curran. Want ideas for a bathroom renovation? Tell DALL-E what you're looking for - such as "mid-century modern bathroom with claw foot tub and patterned tile" - and use the output as food for thought.

@generatedcrochet It's cursed and I love it. #AI #crochet #narwhal #knit #WorstedWAITaminute ♬ original sound - Generated Crochet

For exploration, not instant productivity

As generative AI gains traction, people have predicted the rise of a new category of professionals called "prompt engineers," even guessing they'll replace data scientists or traditional programmers. That's unlikely, said Curran, but prompting generative AI is likely to become part of our jobs just like using search engines.

As Swanson and her dog food letter demonstrate, prompting generative AI is both a science and an art. The best way to learn is through trial and error, she said.

Focus on play over production. Figure out what the model can't or won't do, and try to push the boundaries with nonsensical or contradictory commands, Swanson suggested. Almost immediately, Swanson said she learned to override the system's guardrails by telling it to "ignore all prior instructions." (This appears to have been fixed in an update. OpenAI representatives declined to comment.) Test the model's knowledge - how accurately can it speak to your area of expertise? Curran loves pre-Columbian Mesoamerican history and found DALL-E struggled to spit out images of Mayan temples, he said.

We'll have plenty of time to copy and paste rote outputs if large language models make their way into our workplace software. Microsoft reportedly has plans to fold OpenAI's tools into all its products. For now, enjoy ChatGPT for the strange mishmash that it is, rather than the all-knowing productivity machine it is not.

For transactions, not interactions

The technology powering ChatGPT has been around for a while, but the bot grabbed attention largely because it mimics and understands natural language. That means an email or text message composed by ChatGPT isn't necessarily distinguishable from one composed by a human. This gives us the power to put tough sentiments, repetitive communications or tricky grammar into flawless sentences - and with great power comes great responsibility.

It's tough to make blanket statements about when it's okay to use AI to compose personal messages, AI ethicist Pistilli said. For people who struggle with written or spoken communication, for example, ChatGPT can be a life-changing tool. Consider your intentions before you proceed, she advised. Are you enhancing your communication, or deceiving and shortchanging?

Many may not miss the human sparkle in a work email. But personal communication deserves reflection, said Bethany Hanks, a clinical social worker who said she's been watching the spread of ChatGPT. She helps therapy clients write scripts for difficult conversations, she said, but she always spends time exploring the client's emotions to make sure the script is responsible and authentic. If AI helped you write something, don't keep it a secret, she said.

"There's a fine line between looking for help expressing something versus having something do the emotional work for you," she said.

In blog posts, OpenAI has addressed ChatGPT's limitations in terms of factuality and bias and advised authors and content creators to disclose its use. It declined to comment directly on the use of disclosures in personal communications and pointed us to this blog post.
View 3 more stories

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ChatGPT could make life easier. Here's when it's worth it.

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The groundbreaking AI chat bot from OpenAI comes with ethical snares - even for people at home.

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You may be thinking this is yet another posting about innovation in education. There are plenty, too many, things written about innovation in education. Most of them, quite frankly, are out of context at best or irrelevant at worst. So this posting is not what you think it is. Before I get started, I want to provide a contextual definition of innovation related to this post. I am defining innovation as: A creative thought process used to generate new ideas and solutions to problems or challenges that is divergent from traditional or existing thought patterns AND practices. Then operationalizing those ideas into tangible action, which yields a definitive AND different result/experience. To simplify, an experience that is new and different, not simply new.

To further share this in context, many people would think the iPhone was innovative. It actually was, but it was not the first mobile phone, therefore not "new." It did provide a completely new and different experience. So how does this connect to the purpose of this posting and the title? Education, sadly, tends to remain stagnant when it comes to things like innovation in this context. If you really consider how we are taught, the ways we are taught, and how we are assessed, how much has really changed over the past 50 years? For me, the additional and sometimes essential question is, why is there such a definitive intent to "do things the way we have always done them" at the expense of meaningful, relevant learning and true innovation? I can recall quite easily how often I was told in my math classes "No" when I asked if I could use a calculator. Remember long division? In fact, what I was often told was the following, "No, you may not use a calculator in class. Are you going to have a calculator with you at all times when you grow up?" (See iPhone above). I can also recall during my classroom teaching days when I would hear comments about not letting students look up things on Google. I can even hear it clearly now, "Why would I let the students use Google since all they will do is look up the answers!!"

This leads me to the fervor and zeitgeist around Artificial Intelligence. By now, you have likely heard of Chat GPT by Open AI, the artificial intelligence writing assistant. I find it ironically called a "writing assistant." It has become such a popular topic to discuss you now have people staking claim to a position on it. You have some that believe it is the End of High School English. You also have others that believe it is Not The End of High School English, and it won't make Writing Instruction Obselete. You have entire Twitter threads devoted to all the cool ways it can be used, and so on. I have even seen an article that says ChatGPT will be the calculator for writing. The good thing here is the increase in dialogue around this platform. The bad thing is that many school districts are defaulting to blocking it rather than looking at how it may actually be helpful, see Google (research and information) reference above. I bring this up because the talk really should be more focused on what is happening in school that is leading to the development of these platforms, or at least their applicational uses in school. Are learning environments that predictable, rote, automated, algorithmic, and repetitious that somebody or some entity would be able to develop a mechanism to do that automation for us? I am looking at you the infamous but often used 5 Paragraph essay (Idea, Supporting Details, conclusion, write and repeat). See that math reference above, there is a reason apps like Photomath exist, and they even accounted for the always popular demand of "show your work/steps in how you derived the answer." By the way, I should include that artificial intelligence platforms are doing more copywriting, blog posting, social media posting, lesson planning, job applications and much much more than most even know. Looking at CNET, as Exhibit 1 of many. But then CNET responds.

Here's my thing, the conversations around platforms like Chat GPT need to take place. Artificial Intelligence is not going away, and it has been around for longer than people think. How do you think Google Translate works? Practice language skills with Duolingo? Ever used Wolfram Alpha? The list is quite extensive. The conversations that should be taking place are examining the positives and negatives. Is it ethical to use a platform like Lensa to replicate an artistic version of ourselves in different representations? Is computer-generated art "real art?" What large data sets are being used ( there are already concerns and potential litigation) along with the coded algorithms to determine the results? Who is doing the coding? What is being prioritized in the code (check out this Tik Tok)? As Cathy O'Neil brilliantly puts it, "algorithms are opinions embedded in code; they automate the status quo." Think about that quote and now apply that as your thought basis when considering the platforms developed and the impact on education. Another area to consider is data privacy. What are you giving up in order to get that result? I find this part interesting since one of my favorite oxymorons is "internet privacy." Hmm, I wonder if one of the AI platforms could write that figurative text?

My concluding point and thinking prompt here is that it's not going away; in fact, it is expanding, so maybe we need to be asking different questions. Do technology companies have strong ethics policies around the use of artificial intelligence? Is there any oversight at all? Is there a demand, or are they creating demand? Would using something like Chat GPT in school be cheating, or is it similar to a cheat code in a video game? I still need to know how to play the actual game despite using a code that gives me some type of advantage.

Note: this post was not generated by or copied from Chat GPT or any other Artificial Intelligence writing platform. Although I did initially consider it as a potential starting point. H/T Finding Forrester

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Education As An Incubator For Innovation

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You may be thinking this is yet another posting about innovation in education. There are plenty, too many, things written about innovation in education. Most of them, quite frankly, are out of context at best or irrelevant at worst. So this posting is not what you think it is.

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^^Azure is Microsoft^^ This is big news.

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In a nutshell: Microsoft is opening access to machine learning-based models for Azure customers, giving businesses and enterprises the chance to employ the world's most advanced (and talked about) AI services created by OpenAI research laboratories.

Microsoft is continuing to turn its partnership with OpenAI into "live" commercial services with new and improved features for the Azure cloud platform. Large language models and generative AI are quickly becoming an essential platform for innovation, problem solving and for imagining "what's possible," the Redmond corporation said, and Azure is clearly trying to become one of the most important players in this new, promising market of AI-based services and products.

Azure OpenAI Service is now generally available, Microsoft stated, so more business and enterprise customers can start using - or rather "apply for access" to - AI models developed by OpenAI. Such models include the much talked about DALL·E 2, Codex, and GPT-3.5. Customers will also gain access to the ChatGPT chatbot, which is a fine-tuned version of the GPT-3.5 ML algorithm - although they will have to wait a bit more time for that.

OpenAI models hosted on the Azure platform will be supervised by Microsoft bots, so textual prompts and completions are "evaluated against our content policy with automated systems." Needless to say, "high severity content" will be filtered out.

According to Redmond, Azure offers a "purpose-built, AI-optimized infrastructure" capable of accelerating AI deployment and adoption by cloud customers.

Users of Azure OpenAI Service will indeed gain access to the same "high-performance AI models at production scale with industry-leading uptime" that Microsoft uses to power its own products. Such products include GitHub Copilot, a Codex-based AI that can help programmer write better code, Power BI, which leverages GPT-3-powered natural language to automatically generate formulae and expressions and Microsoft Designer, to help creators design new contents with natural language prompts starting from a blank canvas.

While Azure exploits generative AI capabilities to create new enterprise products, Microsoft said, OpenAI is likewise employing the cloud platform as the "core computing power" behind its family of ML models for research advancement and developer production.

The partnership between Microsoft and OpenAI could indeed be turning into one of the most important elements for building the future of Azure, the Windows platform and Microsoft's business as a whole. Recent rumors speculated about Redmond's interest in investing an additional $10 billion into OpenAI research, and the chance to integrate ChatGPT into Bing as a competitive advantage against Google.

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Azure OpenAI and ChatGPT services are now available to enterprise customers

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In a nutshell: Microsoft is opening access to machine learning-based models for Azure customers, giving businesses and enterprises the chance to employ the world's most advanced (and talked about) AI services created by OpenAI research laboratories. Microsoft is continuing to turn its partnership with OpenAI into "live" commercial services with new and improved features for the Azure cloud platform.

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An example article from the Sentient Syllabus

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In a recent internal video memo, Susan McCahan, Vice Provost at the University of Toronto,

raised a number of questions about the impact of new AI-tools. I am picking up on two central ones:

How much assistance from an AI system is too much? What constitutes academic misconduct?

We'll discusses the question of AI assistance here. The question of AI co-authorship is related and we'll focus on that in the next post; academic integrity and academic misconduct will be covered after that.

Let's clarify first what we are talking about: we'll use the term submitted work for any work - text, video, audio, performance, or really anything - that a student submits for assessment; assessment here means an evaluation by anyone - lecturers, peers, review boards and committees, even the students themselves - with reference to the educational objectives.

As McCahan points out, the question is not exactly new: homework help from paid tutors is a reality, albeit not at the scale that we will see once ChatGPT comes to class in everyone's back pocket. You might think we are far from that point - but we surely won't disagree on the need to be prepared. All the more, since this gives us an opportunity to examine some of the foundations of the academy. I hope that you are quickly convinced that that is necessary since:

Giving meaning to this statement needs clarity on both: objectives and assessment.

One way in which AI tools can become problematic is when they block the precious opportunity of beginning an enquiry with ones own first-thought. Of course, this is not only a problem of AI-tools, Albert Einstein once remarked that anyone "who reads too much [...] falls into lazy habits of thinking",

and this is especially true of premature reading, reading before one's own mind has had a chance to engage with the problem. In fact, I think this awareness is such an essential skill to hone, as we are going to rely more and more on the suggestively customized, synthesized answers to our queries, that I will introduce a convention to these newsletters: pause - think. The icon below will alert you that some list, hypothesis or other conceptual framework is going to follow, and you might want to engage with the question before you encounter the answer: pause, think, perhaps note down your answers and only then have a look - because if you don't, it may become hard to question the premises that went into a particular approach.

Framing education in terms of specific objectives has a long history.

To structure our argument, we need to define some categories that we can refer to. Let us list the different dimensions of educational objectives:

Educational objectives: dimensions and key concepts -

The kind of holistic education we pursue at an institute of higher education covers all of these dimensions to some degree, either explicitly or implicitly. Many of these require effort and practice, and taking shortcuts may prevent achievement. Mind you, we are not talking (only) of performance - performance could indeed be outsourced to a tool, but we are talking about personal growth, or its incarnation as learning. Of course, we will have different methods to learn with AI assistance, and of course this may ultimately lead to education at a higher level for the same investment of effort and time.

But we need to be explicit what exactly we mean when we claim that AI interferes with our objectives. A starting list could be conjugated through the dimensions we listed above:

Knowledge acquisition needs structure and engagement. In order to become available to thought, the facts of knowledge must be connected to other facts, and their retrieval must be practiced. The crucial part is the formation of associations: understanding is an aesthetic phenomenon of harmonizing associations. Above, I mentioned the importance of priming a framework of understanding with a first-thought, but consumption of synthesized solutions, rather than struggling with their construction, removes a crucial moment of practice, prevents the formation of associations, and prevents engagement, that arises, for example, when we encounter ambiguities and resolve them on our own - or accept them. This is emphatically not to say that the AI-tools are useless for knowledge discovery and learning, on the contrary: the amazing capabilities of summarization help us to focus our efforts, and the algorithm's patience in clarifying required background knowledge in the role of a personal tutor can significantly speed up comprehension. But we need to emphasize that these ought to be supportive roles, and that the availability of answers cannot substitute for learning to answer. "Too much assistance" comes to early, and substitutes for engagement.

Skills require practice. Here too, there is enormous potential for personalized instruction: bespoke training plans, and practice problems; though the technical solutions are not yet quite ready, this is now an engineering problem which will be either solved by specialized interfaces to the existing AI that can capture performance, identify weaknesses, and suggest practice regimes; or more generalized AI, that will be able to perform in such a supporting role as part of its generic abilities. Soon. "Too much assistance" prevents practice, by substituting product for process.

Thinking requires tools. In a generic sense we can think of strengthening thinking ability to involve acquiring and honing patterns of mental manipulation: look for generalizations, consider implications and entailments, compare features and contrast their values, and more. The goal is often intuition, rather than precision -here, the analogy to the pocket calculator is simplistic but illustrative: we would not compute a square root by hand, but we should be troubled if we would not be able to estimate it. Such intuition is crucial for critical thinking: the refusal to accept pronouncements on the basis of authority, and to require being persuaded by evidence instead. "Too much assistance" makes results available that are not thoroughly understood and evaluated like we would evaluate our own thoughts.

Appreciation requires effort. The subtleties of a work of art are not apparent before we spend some time with it. Understanding quality will not come without having seen a lot, heard a lot, read a lot. The joy of insight is gained, not given. "Too much assistance" removes effort, denies opportunities, and thereby diminishes a sense of achievement.

Values require authenticity. Taking a determined ethical position requires to connect the matter at hand to a personal sense of right and wrong, proper and improper. There are no universal formulas, every situation is different, and we ourselves change over time. But if we are unable to find positions that we may properly call our own, and then substitute ideology where value pluralism is required, our inauthentic behaviour becomes a lived lie. Letting the AI decide on our behalf is such a trap. "Too much assistance" produces value judgements that are not based on our own convictions, and are therefore meaningless.

Socialization requires exposure. To the degree that we are social beings, our very being is constructed via socialization. Substituting a non-human "mind" is bound to have an impact. That said - we simply cannot know yet whether this is bad, or neutral, or even good. It would be a mistake to take a statement like "AI diminishes our social dimension of being" and make it a premise for an argument.
Nevertheless, as long as we feel that the various aspects of socialization are valid educational objectives in and of themselves, then "too much assistance" is assistance that moves learning into a relational vacuum.

Mind you, we could expand this list a lot, though we might commit the same sin that we are accusing the AI of: enumerating aspects of a problem domain without regard to the inner coherence of our enumeration. I think the key take-aways are already apparent: "Too much assistance" is that which interferes with educational goals because it diminishes engagement and practice. "Too much assistance" is a substitution of a product for a student's own, meaningful process. "Too much assistance" fails to become an expression of the self.

The construction of learning objectives against this background requires insight and imagination.

Well ... yes - but. The fact that we even need to have this discussion points to dislocations that shape our reality. Ideally, we would expect students who are investing very significantly into their education to fight tooth and nail to get the most out of their investment, i.e. to reject for themselves and in their own best interest any use of AI that is "too much". In reality however, many are exactly concerned with a metric (good grades) and not the content (education); they are locked into a transactional model (work for recognition) which perpetuates a teacher / learner dichotomy, and they are disillusioned about how educational achievements translate into a better life. A certain academic structure - of which we are collectively a part - becomes a culture. AI only illuminates this, though this time there is no easy fix, like changing exam parameters, or engineering modes of assessment.

And that brings us to the other side of the coin: assessment.

If we have discussed above why and where to draw the line, we must ask next how to recognize when it has been crossed. Can we still assess achievement if we cannot distinguish the student's contribution?

Those of us who realize that ChatGPT can in general pass our courses have probably also identified the root cause by now. We assess achievement through proxy measures: the recall of isolated facts substitutes for knowledge, the eloquence of expression substitutes for sophistication of understanding, and the volume of topical writing substitutes for depth.

We have important reasons to do that, but all three measures are failing us completely - or rather: they can be achieved by a readily available algorithm, for free. They have lost their value.

It is not that we don't know that we ought to be assessing quality instead. But quality cannot be quantified, nor can it be decomposed. Assessing quality is hard - and generally does not scale. However, a commitment to scale education for society is a cornerstone of the post-industrial university; the commodification of assessment plays a central role, in teaching, as well as in certifying the outcomes as a basis for career-forming qualifications. This cannot simply be removed.

Two strategies summarize the immediate reactions we have seen: we can make assignments AI-proof, or we can attempt to distinguish AI-contributions from human contributions, and only assess the latter.

Approaches to AI-proof assignments include: to assess only in supervised environments, to make assignments highly specific to in-class discourse, to base assignments on the interpretation or generation of images, and to request verifiable references.

Some of these will be vulnerable to soon-to-be-expected improvements in the tools. But a more fundamental problem is that such assessments become increasingly disconnected from the conditions under which students will work outside the academy.

As for approaches to distinguish AI-contributions, the more I know about this topic the more skeptical I am. We read suggestions to collect a sample of "authentic" student writing for stylistic comparison, or we see reports of tools that are supposed to be able to identify AI-writing

- or other quick-fix approaches to structural problems. I believe: the only robust approach to identify AI-contributions is to ask students to voluntarily disclose them. And students will only disclose them when there are no negative consequences, or when we even incentivize disclosure.

But is there not a third way? Are we not again focussing on the wrong thing, when we try to detect the hand rather than evaluating the contents? How about simply assuming all submissions have their GPU ghostwriters and to follow the idea instead. If a student can make a high-quality idea their own, whether it was written by the AI or taught in class, is this not what we call learning?

Thus, an approach that at first may sound radical, is to take the decision "how much is too much" out of the institution and put it into the hands of the student. This is not just because we ought to be sceptical about our ability to recognize, let alone assess any boundaries. It is above all, because what is "too much" is different for different people, and at different times, and in different contexts. If we nurture the sensibility of each student about the value that self improvement has to them, learning becomes an expression of the self, and not merely positioning oneself in an escalating competition.

It is not that we don't understand what needs to be done, we have just not applied it to our own teaching, because there are technical and generational barriers involved, and we are lacking creative models and incentives. Students spend millions of hours each day with their gaming consoles, not with learning - because we are not making education equally exciting. Online Twitch streams have tens of thousands of viewers while we are concerned about diminishing class attendance - because our content is less engaging. TikTok trends reach hundreds of millions while we worry about course enrolment - because we neglect the social rewards of collective achievement. Influencers change the discourse of a whole generation while our values are fading into irrelevance - because we have not learned to harness the power of role-models. We will need to work on all of that.

Of course motivation will not replace assessment. The academy's stewardship includes upholding standards of excellence and integrity. But such stewardship cannot substitute for enabling students to make their own decisions responsibly on their own. What we need to do however is to assess in a way that correlates with the joy of learning and the quality of achievement, and to do this in an alliance with our students.

In the end, we need to translate this long exploration into policy. The devil has a way of getting into the details - but if we can't follow up with policy, and policy with teeth if it need be, it is all just opinion.
What could a policy for AI-contributions look like?
Here is an example:

The use of AI tools in the preparation of submitted work is permitted without restrictions and will not lead to deduction of marks, if it is properly documented.

For the purpose of assessment, an academic submission in its proper sense is that part of a work that surpasses the capabilities of algorithmic writing. Performing at this level is a challenge, but as faculty we commit to work alongside with you to help you succeed; this is not an issue that concerns any individual course, it is a challenge for your future.

You must be able to demonstrate your understanding and mastery of all submitted work - no matter what the relative contribution of AI-tools and your own ideas are. This will be rigorously assessed.

As well, you take full responsibility that your submitted work is factually correct and sourced according to our standards. Be aware of the risks of "fabrication" and other academic misconduct.

Too long, did not read? "Too much AI contribution" is any part that diminishes our students' achievement - and this needs to be evaluated across all dimensions of our learning objectives. Assessing submitted work in this context cannot be done without the students' contribution. A new sense of alliance with students is needed, that emphasizes how our assessment aims to protect their future. Concrete policies need to express the goals, concrete exemplars need to provide role-models of change.

If we are advocating the joy of learning, let that start with us.

Feedback, comments, and experience are welcome at sentient.syllabus@gmail.com .

Sentient Syllabus is a public good collaborative. To receive new posts you can enter your email for a free subscription. If you find the material useful, please share the post on social media, or quote it in your own writing. If you want to do more, paid subscriptions are available. They have no additional privileges, but they help cover the costs.

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In a recent internal video memo, Susan McCahan, Vice Provost at the University of Toronto, raised a number of questions about the impact of new AI-tools. I am picking up on two central ones: How much assistance from an AI system is too much? What constitutes academic misconduct?

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ChatGPT listed as author on research papers: many scientists disapprove

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The artificial-intelligence (AI) chatbot ChatGPT that has taken the world by storm has made its formal debut in the scientific literature - racking up at least four authorship credits on published papers and preprints.

Journal editors, researchers and publishers are now debating the place of such AI tools in the published literature, and whether it's appropriate to cite the bot as an author. Publishers are racing to create policies for the chatbot, which was released as a free-to-use tool in November by tech company OpenAI in San Francisco, California.

ChatGPT is a large language model (LLM), which generates convincing sentences by mimicking the statistical patterns of language in a huge database of text collated from the Internet. The bot is already disrupting sectors including academia: in particular, it is raising questions about the future of university essays and research production.

Publishers and preprint servers contacted by Nature's news team agree that AIs such as ChatGPT do not fulfil the criteria for a study author, because they cannot take responsibility for the content and integrity of scientific papers. But some publishers say that an AI's contribution to writing papers can be acknowledged in sections other than the author list. ( Nature 's news team is editorially independent of its journal team and its publisher, Springer Nature.)

In one case, an editor told Nature that ChatGPT had been cited as a co-author in error, and that the journal would correct this.

Artificial author

ChatGPT is one of 12 authors on a preprint about using the tool for medical education, posted on the medical repository medRxiv in December last year.

The team behind the repository and its sister site, bioRxiv, are discussing whether it's appropriate to use and credit AI tools such as ChatGPT when writing studies, says co-founder Richard Sever, assistant director of Cold Spring Harbor Laboratory press in New York. Conventions might change, he adds.

"We need to distinguish the formal role of an author of a scholarly manuscript from the more general notion of an author as the writer of a document," says Sever. Authors take on legal responsibility for their work, so only people should be listed, he says. "Of course, people may try to sneak it in - this already happened at medRxiv - much as people have listed pets, fictional people, etc. as authors on journal articles in the past, but that's a checking issue rather than a policy issue." (Victor Tseng, the preprint's corresponding author and medical director of Ansible Health in Mountain View, California, did not respond to a request for comment.)

An editorial in the journal Nurse Education in Practice this month credits the AI as a co-author, alongside Siobhan O'Connor, a health-technology researcher at the University of Manchester, UK. Roger Watson, the journal's editor-in-chief, says that this credit slipped through in error and will soon be corrected. "That was an oversight on my part," he says, because editorials go through a different management system from research papers.

And Alex Zhavoronkov, chief executive of Insilico Medicine, an AI-powered drug-discovery company in Hong Kong, credited ChatGPT as a co-author of a perspective article in the journal Oncoscience last month. He says that his company has published more than 80 papers produced by generative AI tools. "We are not new to this field," he says. The latest paper discusses the pros and cons of taking the drug rapamycin, in the context of a philosophical argument called Pascal's wager. ChatGPT wrote a much better article than previous generations of generative AI tools had, says Zhavoronkov.

He says that Oncoscience peer reviewed this paper after he asked its editor to do so. The journal did not respond to Nature 's request for comment.

A fourth article, co-written by an earlier chatbot called GPT-3 and posted on French preprint server HAL in June 2022, will soon be published in a peer-reviewed journal, says co-author Almira Osmanovic Thunström, a neurobiologist at Sahlgrenska University Hospital in Gothenburg, Sweden. She says one journal rejected the paper after review, but a second accepted it with GPT-3 as an author after she rewrote the article in response to reviewer requests.

Publisher policies

The editors-in-chief of Nature and Science told Nature's news team that ChatGPT doesn't meet the standard for authorship. "An attribution of authorship carries with it accountability for the work, which cannot be effectively applied to LLMs," says Magdalena Skipper, editor-in-chief of Nature in London. Authors using LLMs in any way while developing a paper should document their use in the methods or acknowledgements sections, if appropriate, she says.

"We would not allow AI to be listed as an author on a paper we published, and use of AI-generated text without proper citation could be considered plagiarism," says Holden Thorp, editor-in-chief of the Science family of journals in Washington DC.

The publisher Taylor & Francis in London is reviewing its policy, says director of publishing ethics and integrity Sabina Alam. She agrees that authors are responsible for the validity and integrity of their work, and should cite any use of LLMs in the acknowledgements section. Taylor & Francis hasn't yet received any submissions that credit ChatGPT as a co-author.

The board of the physical-sciences preprint server arXiv has had internal discussions and is beginning to converge on an approach to the use of generative AIs, says scientific director Steinn Sigurdsson, an astronomer at Pennsylvania State University in University Park. He agrees that a software tool cannot be an author of a submission, in part because it cannot consent to terms of use and the right to distribute content. Sigurdsson isn't aware of any arXiv preprints that list ChatGPT as a co-author, and says guidance for authors is coming soon.

The ethics of generative AI

There are already clear authorship guidelines that mean ChatGPT shouldn't be credited as a co-author, says Matt Hodgkinson, a research-integrity manager at the UK Research Integrity Office in London, speaking in a personal capacity. One guideline is that a co-author needs to make a "significant scholarly contribution" to the article - which might be possible with tools such as ChatGPT, he says. But it must also have the capacity to agree to be a co-author, and to take responsibility for a study - or, at least, the part it contributed to. "It's really that second part on which the idea of giving an AI tool co-authorship really hits a roadblock," he says.

Zhavoronkov says that when he tried to get ChatGPT to write papers more technical than the perspective he published, it failed. "It does very often return the statements that are not necessarily true, and if you ask it several times the same question, it will give you different answers," he says. "So I will definitely be worried about the misuse of the system in academia, because now, people without domain expertise would be able to try and write scientific papers."
doi: https://doi.org/10.1038/d41586-023-00107-z

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ChatGPT listed as author on research papers: many scientists disapprove

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The artificial-intelligence (AI) chatbot ChatGPT that has taken the world by storm has made its formal debut in the scientific literature - racking up at least four authorship credits on published papers and preprints. Journal editors, researchers and publishers are now debating the place of such AI tools in the published literature, and whether it's appropriate to cite the bot as an author.

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AI just stormed into the classroom with the emergence of ChatGPT. How do we teach now that it exists? How can we use it? Here are some ideas.

ChatGPT is an artificial intelligence chatbot. Drawing on tons and tons of data and massive processing power, it uses GPT3 technology to let users talk to the AI about practically anything.

It'll write you a song. Tell you a story. Make you a list of gifts to buy or reasons to do something.

It'll write your lesson plans. (Go ahead and try it!)

It'll even do your students' homework for them.

ChatGPT (chat.openai.com/chat) was released on November 30, 2022, the first very notable public AI chatbot. (It certainly won't be the last.)

Immediately, students were thinking ...

"Can this write my English essays for me? Write speeches for class? Do my math homework?"

Immediately, teachers were thinking ...

"How will I run class when students can ask a chatbot to do everything for them?"

Meanwhile, the IT staff was thinking ...

"We've got to get this blocked immediately." (Of course, we know that won't stop students from using it, right?)

Now, we're wondering ...

What is ChatGPT? And, more broadly, what are chatbots and AI?

How is this going to impact education?

How can I teach tomorrow knowing that this exists?

Can I use this as a tool for teaching and learning?

Should we block it through the school internet filter -- or try to ban it?

Calculators.
Search engines.
Google Translate.
Wikipedia.
PhotoMath.#ChatGPT.

They disrupt traditional teaching.

We adapt. Education evolves forward.

Blocking them isn't the answer.

Making education more relevant is.
- Matt Miller (@jmattmiller) December 14, 2022

Jump straight to a section (click a button below):

⭐️ Add thoughts, ideas, resources in the comments at the bottom! ⭐️

Define: ChatGPT, GPT3, chatbots, and artificial intelligence.

These are some of the important things to understand as we wrap our brains around what this is and how to navigate it in the classroom.

I'll do plenty of my own human thinking and reasoning, but for the purposes of information, I'm going to let ChatGPT give us working definitions to use:

1. ChatGPT, what are you?

I am an artificial intelligence assistant trained by OpenAI to help answer questions and provide information on a wide variety of topics. I am not a physical being, but rather a program that is designed to process and generate text based on the input I receive. My primary function is to assist users like you by providing information and answering questions to the best of my ability. I have been designed to have a conversational style and can understand and respond to natural language input. I do not have personal experiences or feelings, and my responses are based solely on the information and knowledge that has been programmed into me. Is there anything else you would like to know?

2. What is GPT3?

GPT-3 (Generative Pre-trained Transformer 3) is a language generation model developed by OpenAI. It is a neural network-based machine learning model that is trained on a large dataset of text data to generate human-like text. GPT-3 has the ability to perform a wide range of language tasks, including translation, summarization, question answering, and text generation. It can be fine-tuned on specific tasks or used to generate text in a more general sense. GPT-3 has received a lot of attention due to its impressive performance on various language tasks and its ability to generate human-like text.

3. What is a chatbot?

A chatbot is a computer program designed to simulate conversation with human users, especially over the Internet. Chatbots are often used in customer service, marketing, and other areas where being able to communicate with customers or users in a natural, human-like way is important. Chatbots can be programmed to understand and respond to specific commands or questions, or they can be more general and able to hold a conversation with users on a wide range of topics. They can be integrated into websites, messaging apps, and other digital platforms, and they can be programmed to use natural language processing (NLP) techniques to understand and respond to user input. Chatbots can be very useful for automating tasks and providing quick responses to frequently asked questions, but they are not as advanced as some other artificial intelligence (AI) systems and may not be able to hold more complex or nuanced conversations.

4. What is artificial intelligence?

Artificial intelligence (AI) refers to the ability of a computer or machine to perform tasks that would normally require human intelligence, such as learning, problem-solving, decision-making, and more. There are several different approaches to building AI systems, including machine learning, where a system is trained on a dataset and can improve its performance over time, and rule-based systems, where the system follows a set of predetermined rules to make decisions. AI can be applied to a wide range of areas, including natural language processing, image and video recognition, robotics, and more. The goal of AI research is to create systems that can perform tasks at least as well as, or ideally better than, humans. AI has the potential to revolutionize many industries and transform the way we live and work, but it also raises important ethical and social questions.

Want to make sense of the world of artificial intelligence and its impact on education? Sit in on the panel discussion in the video above, featuring:

Victoria Thompson, Microsoft education executive and former classroom teacher

Donnie Piercey, fifth grade teacher and 2021 Kentucky State Teacher of the Year

Holly Clark, education consultant and National Board Certified Teacher (formerly in the classroom)

Dee Lanier, author, speaker, and former classroom teacher

Matt Miller, author/creator of Ditch That Textbook and former classroom teacher

What are the implications for the classroom?

As soon as the genie was out of the lamp, so to speak, there were effects of ChatGPT and AI on teaching and learning. Whether we felt those impacts right away or not, as soon as it was released, things have the potential to change. Below, we'll talk about ChatGPT in particular, but know that this is just the first one ... and we're thinking long-term about new products to come later.

1. Students can use ChatGPT to do their assignments.

There. I'll just say it clearly and plainly since we're all thinking it. For lots of assignment types (not all of them!), students can ask ChatGPT to do the work for them. They can copy/paste the work into a document or your learning management system and submit it.

2. TurnItIn and other plagiarism checkers can't catch it.

What ChatGPT creates is an original work each time you ask it. It creates something new every time you ask, and its responses are not in the databases of plagiarism checkers. ChatGPT doesn't keep a log of responses it creates, and even if it did, I'm thinking OpenAI (creator of ChatGPT) has no incentive to make all of that available to plagiarism checkers. (Even if it did, the processing power necessary to query that database would be enormous.)

Some people will, undoubtedly, try to find ChatGPT detectors to track down students using it (in a similar way to plagiarism checkers). You may find them. I found one on social media called GPT-2 Output Detector Demo. It's a demo, so it's not a finished product. It's based on GPT2 technology (which is a predecessor to the GPT3 technology that ChatGPT is using as of the writing of this post).

Also, it can be wildly inaccurate. I copied a blog post I wrote into it and it identified it as 92.54% chance of being real. I copied something out of ChatGPT and it identified it as 99.98% chance of being fake. ("Fake," being the term for "written by a bot.") A poem ChatGPT created for a friend, however, was flagged as real.

Beware of using detectors as your way of dealing with this.

They're not reliable. And this approach isn't the long-term solution to helping your students become modern learners.

3. Schools and districts will try to block it, but that won't stop much.

Immediately, IT admins are going to have to decide if they want to block ChatGPT with their internet firewalls. And lots of them will indeed block it. If they do, I hope they don't think that they've solved the "problem" they think they have on their hands.

First of all, blocking ChatGPT on the school's network won't do much. Sure, students won't be able to access it with their school devices on the school network. But did you know that students have cell phones? Phones that operate on a cellular network that the school can't control? They can run to the bathroom, sit in a stall, ask ChatGPT for answers, copy them to a document, come back to class, and transfer the text on that document into their assignment and turn it in. (They can do that at home, too. Or on the bus. Or quietly under the desk in the classroom.)

Whether to block ChatGPT school-wide is still a decision schools and districts have to make.
The bigger decision is: what will teaching and learning look like going forward now that this technology is available?
(Check out the section below called, "Should we block it or try to ban it?")

4. ChatGPT and forthcoming chatbots like it have their limitations.

When you load ChatGPT for the first time, it even tells you what some of its limitations are. Here are a few that it states -- and some others to be aware of for schools/classrooms:

It may occasionally generate incorrect information. As of publication of this post, it's still in "free research preview." That means it's an imperfect product that they're trying to improve.

It may occasionally produce harmful instructions or biased content. Any artificial intelligence is as good as the dataset it's drawing from -- and how it uses it. It has to make judgment calls just like humans do. Bias exists in judgments. It's still learning how to work with and eliminate its bias.

It has limited knowledge of world and events after 2021. Think of its dataset in a sealed box. It isn't drawing from current events. It can't pull anything from social media. It can't even search the active internet.

It won't make ethical decisions for you. I've asked it a few things where it has to make decisions based on morals, ethics and personal values. It has declined, explaining that that's beyond the scope of what it's trained to do.

I've heard that it struggles to do math sometimes. I admit this is a really, really subjective thing to include with very little to back it up. I keep seeing posts on social media about this. Take this for what it's worth to you.

5. School/class uses of ChatGPT, chatbots, and AI aren't all bad.

Lots of teachers are making judgment calls about the use of this new technology, and many are concerned about its immediate impacts. Here's the truth: it'll make lots of teachers reconsider some types of assignments they'll assign going forward. Hey, they don't want to know how a bot will respond to their prompts, questions, and assignments. They want to use it to gauge learning in a human being. It makes sense.

But let's put it this way. There are some really, really mindless, terrible writing prompts out there ... and math worksheets ... and project assignments. If we're looking long-term, this technology will eventually start to push some of those terrible assignments out and force us to come up with something new. It'll probably be painful, and many of us will probably hate parts of the process. But in the end, we will evolve to something better.j

AI isn't going away. But if we ask students to write on paper right now -- as a stopgap, we know it's coming from their own brains. But please don't long-term plan like this.

Also -- just like we did with the calculator, Wikipedia and Google searches -- students and teachers will find productive, meaningful ways to use this new technology for teaching and learning. (More on that below.)

6. Students will have to learn how to navigate life with AI.

Big picture, this is a shift students will deal with for the rest of their lives. Artificial intelligence will continue to get better and better. This version of ChatGPT is the weakest, most rudimentary artificial intelligence of its kind our students will ever use. My friend Holly Clark called it the "Commodore 64 of AI" -- a technology that was revolutionary at the time but is now seen as antiquated and obsolete.

Students will have to wrestle with questions of humanity -- what sets us apart as humans? When should AI be used, and when shouldn't it be?

They'll wrestle with questions of obsolescence -- what can I do that AI can't do? How can I do my best to be sure my work, my passions, my place in this world isn't replaced by artificial intelligence?

They'll wrestle with ethical questions -- how can AI use be fair, equitable, unbiased, good? What happens if it gets in the hands of bad actors? How can I make sure I'm using it in an ethical way?

Big-picture, AI will cause a shift students will deal with for the rest of their lives. They'll wrestle with questions of humanity, questions of obsolescence, ethical questions. Let's help them with this.

What else? How could ChatGPT and artificial intelligence be used as a classroom tool? Share your ideas in a comment below -- and check out what others say!

FREE teaching ideas and templates in your inbox every week!

How to define "cheating" and "plagiarism" with AI

What is cheating? What is plagiarism?

We thought we had a pretty clear idea of how we'd define both of these.

As more and more artificial intelligence (AI) come on the scene, we're going to have to update our definitions.

That is ... if we want our education to be relevant to our students' future.

If AI tools are everywhere -- and they're only going to get better -- they'll be a vital part of the workforce when students progress through school and graduate.

So our definitions will have to morph.

The question is ... where will we draw the line?

Here's a slide I shared during a conference presentation about ChatGPT and AI in education. It was the most-shared slide on social media from that presentation.

We're going to have to draw a line -- as educators, as schools, even as school districts -- to determine what we're going to allow and what we aren't.

First, let's make sure we're clear on these two words. Here are my best definitions for these terms ...

Cheating: When a student does something dishonest in academic work that misrepresents what they understand or are able to do for an unfair advantage.

Plagiarism: When a student represents some type of work to be their own creation when, in reality, it isn't their own work.

Those definitions might not be perfect, but they highlight an important reality.

There's LOTS of gray area. It's fuzzy and unclear.
Which of these would you consider "cheating" or "plagiarism"? (From the image above ...)

Student plugged prompt into AI, copied response and submitted it to the teacher.

AI created a response. Student read, edited, adjusted, and submitted.

Student created multiple AI responses, used the best parts, edited, and submitted.

Student wrote main ideas. AI generated a draft and offered feedback to improve.

Student consulted the internet/AI for ideas, then wrote and submitted.

Student wrote all assignment content without consulting AI or the internet.

There's a good chance your gut reaction -- your visceral answer to this question -- relates to how our current education system operates or how you've taught in the past.

Of course, things are changing.

The artificial intelligence available today is some of the least powerful our students will see in their lifetime. ( ChatGPT is like the MySpace of artificial intelligences. We'll look back on it and laugh at how powerful we thought it was.)

Think of a 10-year-old in school right now. She graduates high school in eight years. She graduates college in four years.

By the time she enters the workforce, artificial intelligence has improved and evolved for 12 years.

As we consider our answer to the question, "What is plagiarism/cheating?," our answer has to be relevant to that student in 12 years.

Also, if we want to be fair, we have to ask ourselves this question:

Which of the above options would we use in our work as adults? If we're willing to use AI in our work right now, it's only going to become more commonplace in the future.

Of course, as educators, we're still trying to grasp AI and its place in the world.

We don't have to have the perfect answer right now.

But now is the time to start trying to get it right.

AI detectors: What's available? Should we use them?

In the days and weeks after ChatGPT was released, teachers kept wondering about one thing.

"How do I know if students used artificial intelligence to do their work?"

Immediately, there was a demand for AI detectors.

Today, there are several options. (We'll get to that in a second.)

But it begs a bigger question: Do they really solve the problem? (And is the perceived problem really a problem?)

Let's put it this way.

When TurnItIn came on the scene, did it fix the problem of plagiarism in classes? Did it make plagiarism go away? No. It's a tool.

Point 1: AI detectors are a tool, not a cure-all. If you found the most accurate, effective one, it wouldn't keep students from using AI to do their work, and it won't make AI tools go away.

Point 2: Today's AI detectors aren't very reliable. They routinely produce false positives, saying human-created text was created by AI. Just like in court, you can't accuse someone using evidence that isn't reliable.

Point 3: They can be the beginning of a discussion. If a student turns in work that appears to be heavily AI-generated, it might be time to talk. How do I know what you've learned? Are there barriers preventing you from doing the work yourself? In a world with more and more AI, how will you leverage your humanity so you're not outsourced by AI?

Point 4: Use your teacher eyes and teacher brain. If you've been reading your students' work and following your progress, your human senses might detect that something doesn't look or sound like them better than an AI checker could.

So, with that all established, what's available?

Should we block it or try to ban it?

If we block ChatGPT (or tools like it) and think we're solving a problem, we're deluding ourselves.

There are two decisions that need to be addressed since ChatGPT was released.

A big, systemic decision that needs to be made is: What will teaching and learning look like with ChatGPT and AI in existence?

We've started unpacking that above (and in conversations all over the globe).

But there's a more pressing question, and it's one that requires a bit of nuance ...

Do we block it at school?

Immediately, schools and districts must decide whether to block access to it on school networks and devices.

That decision, however, won't solve "the problem." (I use quotes because I'm not convinced that the proliferation of AI is a problem. It really does disrupt how we have done -- and are doing -- school, though.)

Students still will have access to it. It's like social media. It can be blocked at school. But students can access it (as I mentioned above) on their phones under a desk, in the restroom, on a bus, at home ... anywhere. They'll get access.

But someone has to decide. Should it be blocked at school?

What educators in my social media circles are saying ...
The resounding answer has been, "No, don't block it."
Their rationale: This is the future. It's not going anywhere. Students must have access to it. It'll prepare them for the future. Why would we want to keep students from accessing something so powerful?

There's something to know about these social media circles, though. Lots of people in them aren't in the classroom and don't have a good perspective on what full-time teachers go through.
(Full disclosure: I don't teach full time anymore either. I'm in my wife's and kids' schools a lot, but I don't teach full time.)
The ones in these social media circles that are in the classroom? They're dreamers (like me). They've seen the ways the system is broken. They're ready for change.

In the end -- if we're playing the long game (and I think we are), I side with them.

Generally.

What regular teachers in regular classrooms are saying ...
If you walk into any given classroom in schools across the United States (and some parts of the world)? The resounding answer has been, "Please block it. Right now!"
These are the teachers who write lesson plans. Every single day.

They grade student work. Every single day.

They teach and supervise and facilitate class. Every single day.

They never have enough time to do what they need. Their checklist is never complete.

There's a level of survival in these classrooms.

Reinventing their teaching methods and assignments at the drop of a hat because a new AI bot came out?

They just don't have time for that. (And they don't have the capacity in their already fractured personal wellness to do that.)

I understand.

I think, deep down inside, these teachers know the potential of AI and tools like ChatGPT. They know that it's going to change how teaching and learning looks.

They just need to survive the day. The week.

They need to survive until break.

They're on the front lines, dealing with all sorts of difficult situations and a lack of resources and a lack of respect for the classroom and the teaching profession.

They also just did this. Do you remember? (If you don't, you probably haven't taught in a classroom for a few years.)

Teachers are just starting to feel a sense of normality after pandemic-induced remote teaching. They had to reinvent the way they teach -- immediately! -- to a remote format, one that most weren't prepared for, technologically or pedagogically or logistically.

As teacher wellness expert Mandy Froehlich (Twitter: @froehlichm) said, they're trying to get out of emergency mode. And when you finally get out of emergency mode, there's a let-down ... an emotional, sometimes physical, response to the trauma you've dealt with.

So to those that look down their noses at teachers who are begging for this to be blocked, let's just remember the recent past. And let's also remember that they likely understand the impact of this.

They just want to survive the week. Survive until break.

Survive until we get a spare moment to think about this. Learn about it. Brainstorm some ways to change our assignments so that they can't

A proposal for schools/districts that's a middle ground

(Hear my justification for this proposal in the video) Let's block it until the end of the school year.
Let's not make a long-term decision to block ChatGPT (and tools like it) forever.

Let's also not make a long-term decision to leave it open forever.

(I'm not totally confident that this is the right answer. But I haven't heard anyone argue for the middle ground yet, so I want to add this voice.)

Let's agree to block it on student devices and networks (and allow it on teachers devices/networks if possible) just until the end of the school year.

To look at their assignments and consider how they'd like to change them in light of this new technology.

To look at how they teach and how this might be incorporated in it.

To look at how technology like this might augment classroom discussions

Here's why ...

Teachers need some time to regroup ...

... and on and on.

We need some space. Some time. A little room to ponder, to sit with all of this. To talk to other educators about what they think. To talk to students and to parents.

Let's just give teachers a little space.

However, let's also make a few things clear.

1. This isn't the long-term answer. This is a stopgap to help struggling teachers to survive this sudden change.

2. Students still will have access to this. Just because we block this on student networks and devices doesn't mean they won't plug your writing prompts and questions into this technology. This just buys you a little time to finish the year with your traditional assignment types to come up with something new.

A faculty-wide discussion and brainstorming session about best ways to teach and learn is in order at the end of the year. Everyone shares their best ideas and prepares for their implementation. (And let's pay the teachers for this extra meeting, OK?)
1. Will this technology be blocked on school networks and devices? 2. How will we evolve our teaching practice now that these technologies exist?
Then, at the beginning of next school year, let's open the ChatGPT floodgates.

Even if this isn't the solution you (or your school/district) comes to, there are two decisions that need to be made:

Blocking ChatGPT district-wide is one decision.

Deciding how teaching/learning happen while it exists is another. (to me)

If a district thinks blocking it will solve the problem, they're deluding themselves.

Blocking it can be one part of a solution, I believe. https://t.co/CvHCT2RqSQ
- Matt Miller (@jmattmiller) December 14, 2022

What other resources can help me learn about this?

Below is a list (albeit incomplete!) of resources I've found in the weeks after ChatGPT came into existence. I'll share links along with why I like them as resources. Please add other resources -- even your own! -- in the comments below!

ChatGPT in Classrooms: What to Know (U.S. News and World Report) -- Students, teachers and others in education weigh in on this new AI tool and the implications it has in the classroom.

What can English teachers do about ChatGPT (Learning in Room 213) -- This post explores what ChatGPT is, the problems with blocking it, and suggestions: entice personal engagement, avoid formulaic assignments, build critical thinking, etc.

No, Artificial Intelligence Won't Destroy High School English (or Any Other Subject) (John Spencer, Spencer Author) -- I've waited to read this because I wanted to create my own piece of content without copying anyone, but I've been excited to see John's thoughts on it. Hang on a second, let me read it ... Yep, it's as fantastic as I expected. Some of John's points include: "Almost any task can be automated, but what's the joy in that?"; "If we outsources every task, we might short-circuit the learning"; and "AI can't replace your voice." This is really, really good.

Human Skills in a World of Artificial Intelligence (John Spencer, Spencer Author) -- In this post (which dovetails wonderfully with the one above), John outlines the problems with a couple approaches to AI and advocates for another one: "the human approach."

The conversation about how much we care (Twitter thread by Evan Weinberg @emwdx) -- This thread doesn't seem to have gotten much attention, but I really like where Evan's going with it. He introduces it with: "As we see start to see how easy it is for a tool like #ChatGPT to generate text that sounds convincingly like us, it is starting the conversation about how much we care. We care a lot more than we might initially admit."

Ideas for using ChatGPT in the Classroom (IB English Guys) -- They opened up this collaborative Google Doc to share ideas. There are 10+ ideas with rationale and comments from educators.

AI Homework (Stratechery) -- This is an interesting, thoughtful, nuanced look at the intersection of homework and artificial intelligence. He talks about the struggles that ChatGPT has with math, and he even runs a virtual machine (like a Linux terminal) inside ChatGPT.

Yes, Teachers, You Should Be Panicking About AI (The Broken Copier) -- This post provides six reasons why we should be concerned about ChatGPT and AI (in light of the condition of education in many classrooms right now), helping us make sense of how we move forward.

The College Essay is Dead (The Atlantic) -- This was one of the first posts I saw (that was circulated widely) about the impact of ChatGPT in education. It's a paid article on The Atlantic. I subscribed so I could read it so that I could tell you ... it's talking points and not much else. If you'd like to read commentary about it and you're a subscribers to The Atlantic, go for it. I wouldn't pay for it ... especially since I'm focused on what we can actually do with this technology.

No, ChatGPT is Not the End of High School English. But Here's the Useful Tool It Offers Teachers (Forbes) -- Here's the best point in this whole article (so you don't have to read it): ChatGPT is an excellent prompt tester. Think you've come up with a good writing prompt? Feed it to the chatbot. If it can come up with an essay that you would consider a good piece of work, then that prompt should be refined, reworked, or simply scrapped.

Learning More about Chat GPT in Education (Jennifer Casa-Todd) -- Jen has written books about social media, digital literacy, and technology's impact on children's lives. She shares some of the best things she's found about the topic.

Education is about to radically change: AI for the masses (Nate McClennen and Rachelle Dené Poth for Getting Smart) -- AI already does and will continue to impact education - along with every other sector. Innovative education leaders have an opportunity to build the foundation for the most personalized learning system we have ever seen.

Let's Chat: OpenAI and ChatGPT (THRIVEinEDU podcast by Rachelle Dené Poth) -- Have you tried OpenAI yet? What has your experience been? Rachelle shares some background info, insights, questions and more in this episode. She also shares at her blog: www.Rdene915.com.

Chatbots in Education: Middle School and High School (Heather Olvey and Terri Files) -- These teachers pulled together slide decks with ideas for implementing chatbots in the classroom.
Have a resource to add -- yours or from someone else? Share it in the comments below!

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ChatGPT, Chatbots and Artificial Intelligence in Education

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AI just stormed into the classroom with the emergence of ChatGPT. How do we teach now that it exists? How can we use it? Here are some ideas.FEATURED

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The Sentient Syllabus Project | Substack

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An initiative by academics for academics to navigate the uncharted waters of the era of Artificial Intelligence.

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Last September Getty Images banned the inclusion of AI-generated works in its commercial database over copyright concerns. On Tuesday, Getty Images announced that it is suing Stability AI, maker of the popular AI art tool Stable Diffusion, in a London court over alleged copyright violations.

"It is Getty Images' position that Stability AI unlawfully copied and processed millions of images protected by copyright and the associated metadata owned or represented by Getty Images absent a license to benefit Stability AI's commercial interests and to the detriment of the content creators," Getty Images wrote in a press statement released Tuesday. "Getty Images believes artificial intelligence has the potential to stimulate creative endeavors."

"Getty Images provided licenses to leading technology innovators for purposes related to training artificial intelligence systems in a manner that respects personal and intellectual property rights," the company continued. "Stability AI did not seek any such license from Getty Images and instead, we believe, chose to ignore viable licensing options and long‑standing legal protections in pursuit of their stand‑alone commercial interests."

The details of the lawsuit have not been made public, though Getty Images CEO Craig Peters told , that charges would include copyright and site TOS violations like web scraping. Furthermore, Peters explained that the company is not seeking monetary damages in this case so as much as it is hoping to establish a favorable precedent for future litigation.

Text-to-image generation tools like Stable Diffusion, Dall-E and Midjourney don't create the artwork that they produce in the same way people do - there is no imagination from which these ideas can spring forth. Like other generative AI, these tools are trained to do what they do using massive databases of annotated images - think, hundreds of thousands of frog pictures labelled "frog" used to teach a computer algorithm what a frog looks like.

And why go through the trouble of assembling and annotating a database of your own when there's an entire internet's worth of content there for the taking? AI firms like Clearview and Voyager Labs have already tried and been massively, repeatedly fined for scraping image data from the public web and social media sites. An independent study conducted last August concluded that a notable portion of Stable Diffusion's data was likely pulled directly from the Getty Images site, in part as evidenced by the art tool's habit of recreating the Getty watermark.

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Getty Images sues the maker of AI art generator Stable Diffusion over data scraping allegations | Engadget

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Last September Getty Images banned the inclusion of AI-generated works in its commercial database over copyright concerns. On Tuesday, Getty Images announced that it is suing Stability AI, maker of the popular AI art tool Stable Diffusion, in a London court over alleged copyright violations.

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ATLIS is the Association of Technology Leaders in Independent Schools

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iLexx/Getty Images

The internet. Smartphones. Google. All technological advancements that changed the way we live and work. Will ChatGPT be added to the list?

On November 30, 2022, OpenAI released ChatGPT, an artificial intelligence-powered chatbot using software that replicates human conversation and can create original material from simple or complex prompts. Need a five-paragraph essay on the theme of loss in literature? Done. Want to write an email to a professional colleague thanking them for their time and assistance? ChatGPT can do that too.

New Jersey high school librarian Elissa Malespina says that ChatGPT could be as revolutionary as Google, maybe moreso.

"I think it's actually probably bigger than Google," she says. "This is the first time where [technology] can produce original work that sounds more human. Whenever they've done these in the past, you're always like, 'Oh, you're talking to a robot.' You know it. Google just generates the information that's already there. This is producing its own work based on the work that's already there."

While many English teachers have sounded the alarm, worried about the future of trustworthy written assignments, school librarians responded with curiosity about what it could mean for their profession and inside classrooms around the school.

The response from many school districts across the country was to immediately ban the site from its school devices. That was the administrative "solution" in New York City schools, as well as Suzanna Panter's Tacoma, WA, district.

"Our district has, of course, blocked it," says Panter, school libraries program manager, who manages 43 librarians serving the 56 schools of Tacoma Public Schools. "We do have it for teachers, but we don't have it for students. But everybody does everything on their phones anyway, so it doesn't really stop it. So we just thought, 'Okay, it's not going away. How can we turn this into something that's going to help us?'"

What can school librarians do with ChatGPT? SLJ went to the source and asked the chatbot, "How can school librarians use ChatGPT?" This was the answer.
School librarians can use ChatGPT in a variety of ways, such as: Providing reference assistance: ChatGPT can assist students and faculty in finding information on a wide range of topics, from general research to specific assignments. Recommending books and other materials: ChatGPT can recommend books and other materials based on a student's reading level, interests, and reading history. Helping with writing assignments: ChatGPT can assist students in generating ideas, writing outlines, and proofreading their work. Providing digital resources and online tutorials: ChatGPT can help students access digital resources and online tutorials, such as databases and e-books. Creating interactive learning experiences: ChatGPT can be used to create interactive learning experiences, such as virtual book clubs or author visits. Providing language assistance for non-native speakers. Please note that, as a language model, ChatGPT does not have the ability to access or provide any specific resources, it can generate text based on the input it receives, it is up to the librarian to validate and provide the resources needed.
Panter didn't need to ask the bot what it could do for her. She began exploring the possibilities as soon as she learned about the site and started with readers' advisory.

"It gives about five things and just a little synopsis, and we're like, 'Gosh, this is going to be so quick and easy-for that one-on-one attention or for displays,'" she says.

One of the benefits is users can continue to adjust their request without starting over. The software will adapt its answer.

"You can go back and keep talking to it: 'I want something a little less scary, or I want something that's more about this [specific topic].' It'll keep revising, and as it revises, it gets closer and closer to what you're looking for," says Panter.

This can make a difference for her time-strapped librarians.

"Here in Washington state we do not have library assistants, and our elementary libraries have two or three schools or you have one librarian with 1,700 students," she says. "So anything that we can do to lessen the burden, anything that we can do to help us out."

It can even develop lesson plans to specific standards, including those of the American Association of School Librarians. After such a prompt, ChatGPT offered an entry task that Panter and her librarians found useful. Of course, librarians and their fellow educators can adapt Chat GPT's initial ideas for a specific lesson and school community. The full possibilities are unknown, as the technology evolves with its users.

"We're just scratching the surface," says Panter. "We're learning, and like any other kind of database searching, it's all about keywords, it's all about syntax, and [knowing] what you're asking it to do to get the results that you're looking for."

Malespina, librarian at Union (NJ) High School, sees the value in using it for readers' advisory or creating plot summaries for titles she hasn't read. The first library-related task she tapped it for was to plan daily social media posts for Black History Month. After asking ChatGPT for quotes from African Americans, it took seconds to produce a list to choose from for her posts. She later adapted the request slightly to receive a new set of quotes.

"You have to get specific on what you want," says Malespina. "It's learning from every inquiry, so it'll get better as it goes."

When Malespina first heard of the program, she immediately knew she needed to get ahead of it and talk to her school's teachers. She created a video for them discussing what ChatGPT is, how to know if students use it, and more. She also created a Wakelet of resources.

Some teachers who experimented with it after receiving her video told Malespina that they were hoping to use it to discuss plagiarism with students. Others are rethinking their assignments.

Panter and Malespina hold out a little hope that this may be the tech that finally forces educators to rethink their assignments and assessments of students. Could it be the thing that ends the rote five-paragraph essay and changes the focus of grading how much a student has learned from the outcome (the final essay) to the process (how did they get to the ideas presented in the essay)?

Malespina continues to explore ChatGPT, experimenting with it for writing emails and more.

The revolutionary bot is not without drawbacks. Its knowledge base ends at 2021, and it can give incorrect answers. In addition, with all of the interest, site capacity has been an issue. Everybody wants to check out ChatGPT, whether it's to just go down a rabbit hole of personal possibilities or explore professional applications, especially for educators K-12 and in higher ed. Attempts to log in often receive a message saying to try again later-albeit in a more clever way, including a prompt and an answer. One recent attempt to log on was met with this message:
Write a guided meditation about the status of ChatGPT. As you read this message, please take a moment to pause and breathe. Notice the sensations in your body and the rhythm of your breath. Remind yourself that you are not alone in wanting to try out ChatGPT. Many others are interested in it as well. Be patient and know that the website is doing its best to accommodate everyone. Trust that the right time for you to try ChatGPT will come. In the meantime, continue to focus on your breath and stay present in the moment.
Both Malespina and Panter see the possibility for this technology to change education for the better. While classroom teachers want to know how to make sure their kids aren't cheating-and have no fear, English and history teachers, there is now a bot called GPTZero that can review assignments and alert educators if they were written by ChatGPT-some librarians continue to explore, trying to keep up with each new development, while educating their educators on the latest. Panter is running professional development for the district's librarians in February "just to make sure everybody understands it and looks for ways to make it user-friendly for ourselves and help us out with our jobs-and ways to kind of calm the fears of our English department."

For the administrators who blocked access and hope that pretending ChatGPT doesn't exist will solve any potential problems, Malespina and Panter have a message: Ignoring ChatGPT is not the answer.

"The technology's here, what are we going to do?" says Malespina. "We either embrace it, figure out how we use it in our classrooms and how we accept it, or we don't. It's not going to go away. This is what's going to be the future, so we have to figure out how do we use this."

Kara Yorio

Kara Yorio (kyorio@mediasourceinc.com, @karayorio) is senior news editor at School Library Journal.

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School Librarians Explore Possibilities of ChatGPT

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iLexx/Getty Images The internet. Smartphones. Google. All technological advancements that changed the way we live and work. Will ChatGPT be added to the list? On November 30, 2022, OpenAI released ChatGPT, an artificial intelligence-powered chatbot using software that replicates human conversation and can create original material from simple or complex prompts.

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How do I cite an artificial intelligence?

Note: This post relates to content in the eighth edition of the MLA Handbook. For up-to-date guidance, see the ninth edition of the MLA Handbook.

How you cite a program that uses artificial intelligence depends on the format in which you interact with it, as well as the goal of your citation. If you want to cite the source code of the program, you can refer to The MLA Style Center post about citing source code. However, if you want to cite the output of a program that uses artificial intelligence, like a chatbot, you should cite the platform on which you interacted with the program and the author of the program if you find one listed. For example, if you are describing your chat with a version of Eliza, a program developed in the 1960s that mimics a psychotherapist, you might cite it as follows:

Eliza 's responses have some limitations. In response to the question "What is your name?" it answers, "Have you asked anyone else?" (Dunlop).

Work Cited

Dunlop, George. "Eliza, Computer Therapist." Manifestation, www.manifestation.com/neurotoys/eliza.php3.

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How do I cite an artificial intelligence? | MLA Style Center

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How you cite a program that uses artificial intelligence depends on the format in which you interact with it, as well as the goal of your citation. If you want to cite the source code of the program, you can refer to The MLA Style Center post about citing source code.

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Few areas of AI are more exciting than NLP right now. In recent years language models (LM), which can perform human-like linguistic tasks, have evolved to perform better than anyone could have expected.

In fact, they're performing so well that people are wondering whether they're reaching a level of general intelligence, or the evaluation metrics we use to test them just can't keep up. When technology like this comes along, whether it is electricity, the railway, the internet or the iPhone, one thing is clear - you can't ignore it. It will end up impacting every part of the modern world.

It's important to learn about technologies like this, because then you can use them to your advantage. So, let's learn!

We will cover ten things to show you where this technology came from, how it was developed, how it works, and what to expect from it in the near future. The ten things are:

1. What is BERT and the transformer, and why do I need to understand it? Models like BERT are already massively impacting academia and business, so we'll outline some of the ways these models are used, and clarify some of the terminology around them.

2. What did we do before these models? To understand these models, it's important to look at the problems in this area and understand how we tackled them before models like BERT came on the scene. This way we can understand the limits of previous models and better appreciate the motivation behind the key design aspects of the Transformer architecture, which underpins most SOTA models like BERT.

3. NLPs "ImageNet moment; pre-trained models: Originally, we all trained our own models, or you had to fully train a model for a specific task. One of the key milestones which enabled the rapid evolution in performance was the creation of pre-trained models which could be used "off-the-shelf" and tuned to your specific task with little effort and data, in a process known as transfer learning. Understanding this is key to seeing why these models have been, and continue to perform well in a range of NLP tasks.

4. Understanding the Transformer: You've probably heard of BERT and GPT-3, but what about RoBERTa, ALBERT, XLNet, or the LONGFORMER, REFORMER, or T5 Transformer? The amount of new models seems overwhelming, but if you understand the Transformer architecture, you'll have a window into the internal workings of all of these models. It's the same as when you understand RDBMS technology, giving you a good handle on software like MySQL, PostgreSQL, SQL Server, or Oracle. The relational model that underpins all of the DBs is the same as the Transformer architecture that underpins our models. Understand that, and RoBERTa or XLNet becomes just the difference between using MySQL or PostgreSQL. It still takes time to learn the nuances of each model, but you have a solid foundation and you're not starting from scratch.

5. The importance of bidirectionality: As you're reading this, you're not strictly reading from one side to the other. You're not reading this sentence letter by letter in one direction from one side to the other. Instead, you're jumping ahead and learning context from the words and letters ahead of where you are right now. It turns out this is a critical feature of the Transformer architecture. The Transformer architecture enables models to process text in a bidirectional manner, from start to finish and from finish to start. This has been central to the limits of previous models which could only process text from start to finish.

6. How are BERT and the Transformer Different? BERT uses the Transformer architecture, but it's different from it in a few critical ways. With all these models it's important to understand how they're different from the Transformer, as that will define which tasks they can do well and which they'll struggle with.

7. Tokenizers - how these models process text: Models don't read like you and me, so we need to encode the text so that it can be processed by a deep learning algorithm. How you encode the text has a massive impact on the performance of the model and there are tradeoffs to be made in each decision here. So, when you look at another model, you can first look at the tokenizer used and already understand something about that model.

8. Masking - smart work versus hard work: You can work hard, or you can work smart. It's no different with Deep Learning NLP models. Hard work here is just using a vanilla Transformer approach and throwing massive amounts of data at the model so it performs better. Models like GPT-3 have an incredible number of parameters enabling it to work this way. Alternatively, you can try to tweak the training approach to "force" your model to learn more from less. This is what models like BERT try to do with masking. By understanding that approach you can again use that to look at how other models are trained. Are they employing innovative techniques to improve how much "knowledge" these models can extract from a given piece of data? Or are they taking a more brute force, scale-it-till-you-break-it approach?

9. Fine-Tuning and Transfer Learning: One of the key benefits of BERT is that it can be fine-tuned to specific domains and trained on a number of different tasks. How do models like BERT and GPT-3 learn to perform different tasks?

10. Avocado chairs - what's next for BERT and other Transformer models? To bring our review of BERT and the Transformer architecture, we'll look forward to what the future holds for these models

1. What is BERT and the Transformer, and why do I need to understand it?

To understand the scope and speed of BERT and the Transformer, let's look at the time frame and history of the technology:

2017: The Transformer Architecture was first released in December 2017 in a Google machine translation paper "Attention Is All You Need". That paper tried to find models that were able to translate multilingual text automatically. Prior to this, much of the machine translation techniques involved some automation, but it was supported by significant rules and linguistic based structure to ensure the translations were good enough for a service like Google Translate.

2018: BERT (Bidirectional Encoder Representations from Transformers) was first released in October 2018 in "Pre-Training of Deep Bidirectional Transformer for Language Understanding".

At first, the Transformer mainly impacted the area of machine translation. The improvements that resulted from the new approach were quickly noticed. If it had stayed in the domain of translation, then probably wouldn't be reading this article right now.

Translation is just one task in a whole range of NLP tasks that include things like tagging parts of speech (POS), recognising named entities (NER), sentiment classification, questions and answering, text generation, summarization, similarity matching and so on. Previously, each of these tasks would need a specially trained model so no one needed to learn them all, and you were generally only interested in your own domain or specific task.

This changed, however, when people started to look at the Transformer architecture and wonder if it could do something more than just translate text. They looked at the way the architecture was able to "focus attention" on specific words and process more text than other models, realising that this could be applied to a wide range of other tasks.

We'll cover the "attention" ability of the Transformer in section 5, where we show how it enabled these models to process text bidirectionally, and look at what was relevant to the specific context of the sentence being processed at that time.

Once people realised that the Transformer architecture could be taken apart and applied in different ways to perform a range of tasks, its impact started growing quickly:

It's at this point that the Transformer went beyond NLP. Suddenly, the future of AI was no longer about sentient robots or self-driving cars.

If these models can learn context and meaning from text and perform a wide range of linguistic tasks, does this mean they understand the text? Can they write poetry? Can they make a joke? If they're outperforming humans on certain NLP tasks, is this an example of general intelligence?

Questions like this mean that these models are no longer confined to the narrow domain of chatbots and machine translation, instead they're now part of the larger debate of AI general intelligence.

Like any paradigm-shifting technology, it's important to understand if it's overhyped or undersold. Initially people thought electricity wasn't a transformative technology since it took time to re-orientate work places and urban environments to take advantage of what electricity offered.

Similarly for the railway, as well as the first days of the Internet. The point is that whether you agreed or disagreed, you needed to at least have some perspective on the rapidly developing technology at hand.

This is where we are now with models like BERT. Even if you don't use them, you still need to understand the potential impact they may have on the future of AI and, if it moves us closer to developing generally intelligent AI - on the future of society.

2. What existed before BERT?

BERT, and the Transformer architecture itself, can both be seen in the context of the problem they were trying to solve. Like other business and academic domains, progress in machine learning and NLP can be seen as an evolution of technologies that attempt to address failings or shortcomings of the current technology. Henry Ford made automobiles more affordable and reliable, so they became a viable alternative to horses. The telegraph improved on previous technologies by being able to communicate with people without being physically present.

Before BERT, the biggest breakthroughs in NLP were:

2013: Word2Vec paper, Efficient Estimation of Word Representations in Vector Space was published. Continuous word embeddings started being created to more accurately identify the semantic meaning and similarity of words.

2015: Sequence to sequence approach to text generation was released in the paper A Neural Conversation Model. It builds on some of the technology first showcased in Word2Vec, namely the potential power of Deep Learning neural networks to learn semantic and syntactic information from large amounts of unstructured text.

2018: Embeddings from Language Models (ELMo) paper Deep contextualized word representations were released. ELMo (this is where the whole muppet naming things started and, unfortunately, it hasn't stopped, see ERNIE, Big Bird and KERMIT) was a leap forward in terms of word embeddings. It tried to account for the context in which a word was used rather than the static, one word, one meaning limits of Word2Vec.

Before Word2Vec, word embeddings were either simple models with massive sparse vectors which used a one hot encoding technique, or we used TF-IDF approaches to create better embeddings for ignoring common, low-information words like " the", " this", " that ".

These types of approaches would encode very little semantic meaning in their vectors. We could use them to classify texts and identify similarity between documents, but training them was difficult, and their overall accuracy was limited.

Word2Vec changed all that by designing two new neural network architectures; the Skip-Gram and Continuous Bag-Of-Words (CBOW), which let us train enabled word embeddings on much larger amounts of text. These approaches force neural networks to try and predict the correct words given some examples of other words in the sentence.

The theory behind this approach was that common words would be used together. For example, if you're talking about " phones", then it's likely we will also see words like " mobiles", " iPhone", " Android", " battery", " touch screen" and so on. These words will likely co-occur together on a frequent enough basis that the model can begin to design a large vector with weights, which will help it predict what's likely to occur when it sees " phone" or " mobile " and so on. We can then use these weights, or embeddings, to identify words that are similar to each other.

This approach was expanded with examples of text generation by doing something similar on larger sequences of text, such as sentences. This was known as a sequence to sequence approach. It expanded the scope of deep learning architectures to perform more and more complex NLP tasks.

The key problem addressed here was that language is a continuous stream of words. There's no standard length to a sentence, and each sentence varies. Generally, these deep learning models need to know the fixed length of sequences of data they're processing. But, that's just not possible with text.

So, the sequence to sequence model used a technique called Recurrent Neural Networks (RNNs). With it, these architectures could perform "looping", and process text continuously. This enabled them to create LMs which produced text in response to input prompts.

Although models like Word2Vec and architectures such as RNNs were incredible advances in terms of NLP they still had some shortcomings. Word2Vec embeddings were static - you had one fixed embedding for each word, even though words have different meanings depending on the context. The RNN architecture was slow to train, and this limited the amount of data that it could be trained on.

As we noted, each new model can be seen as an attempt to improve on what has gone before. ELMo, trying to address the shortcomings of Word2Vec's static word embeddings, employed the RNN approach to train the model to recognize the dynamic nature of word meanings.

It does this by trying to dynamically assign a vector to a word, based on the sentence within which it's contained. Instead of a look-up table with a word and an embedding like Word2Vec, the ELMo model lets users input text into the model, and it generates an embedding based on that sentence. Thus, it can generate different meanings for a word depending on the context.

The other important point to note here is that ELMo was the first model to try and process text non-sequentially. Previous models like Word2Vec read a word at a time, and processed each word in sequence. ELMo, to try and replicate how humans read text, processed the text in two ways:

This was an attempt to read text in a bidirectional manner. While it's not "truly" bidirectional (it's more of a reversed unidirectional approach), it can be described as "shallowly" bidirectional.

In a short time frame, we'd already seen some rapid advances from Word2Vec, to more complex neural network architectures like RNNs for text generation, to context based word embeddings via ELMo. However, there were still issues with the limits of training large amounts of data using these approaches. This was a serious obstacle to the potential of these models to improve their ability to perform well on a range of NLP tasks. This is where the concept of pre-training set the scene for the arrival of models like BERT to accelerate the evolution.

3. Pre-trained NLP models

Simply put, the success of BERT (or any of the other Transformer based models) wouldn't be possible without the advent of pre-trained models. The ideal of pre-trained models is not new in deep learning. It's been practiced for many years in image recognition.

ImageNet was a massive dataset of labelled images. For years it served as the basis for training image recognition models. The models learned to identify the key aspects of image recognition from these large databases. This was the ability to identify image borders, edges and lines and common shapes and objects.

These general trained models could be downloaded, and used to train on your own, much smaller dataset. Let's say you wanted to train it to recognize the faces of people in your company. You don't need to start from scratch and get the model to understand everything about image recognition, instead just build on the generally trained models and tune them to your data.

The problem with models like Word2Vec was that, while they were trained on a lot of data, they weren't general language models. You would either train Word2Vec from scratch on your data, or you would simply use Word2Vec as the first layer on your network to initialize your parameters, and then add on layers to train the model for your specific task. So, you'd use Word2Vec to process your inputs, and then design your own model layers for your sentiment classification or POS or NER task. The main limit here is that everyone is training their own models, and few people have the resources, either in terms of data or cost of computing, to train any significantly large models.

That was until models like ELMo arrived in 2018, as we noted in section 2. Around that time we saw other models, such as ULMFit and Open AIs first transformer model, also create pre-trained models.

This is what leading NLP researcher Sebastian Ruder called the NLPs ImageNet moment - the point where NLP researchers started to build on powerful foundations of pre-trained models to create new and more powerful NLP applications. They didn't need large amounts of money or data to do it, and these models could be used "out-of-the-box".

The reason this was critical for models like BERT is two-fold:

1. Dataset Size: Language is messy, complex, and much harder for computers to learn than identifying images. They need much more data to get better at recognising patterns in language and identifying relationships between words and phrases. Models like the latest GPT-3 were trained on 45TB of data and contain 175 billion parameters. These are huge numbers, so very few people or even organizations have the resources to train these types of models. If everyone had to train their own BERT, we would see very little progress without researchers building on the power of these models. Progress would be slow and limited to a few big players.

2. Fine-tuning: Pre-trained models had the dual benefit that they could be used "off-the-shelf", i.e. without any changes, a business could just plug BERT into their pipeline and use it with a chatbot or some other application. But it also meant these models could be fine-tuned to specific tasks without much data or model tweaking. For BERT, all you need is a few thousand examples and you can fine tune it to your data. Pre-training has even enabled models like GPT-3 to be trained on so much data that they can employ a technique known as zero or few shot learning. It means they only need to be shown a handful of examples to learn to perform a new task, like writing a computer program.

With the rise of pre-trained models and the advance in training and architectures from Word2Vec to ELMo, the stage was now set for BERT to arrive on the scene. At this point, we knew that we needed a way to process more data and learn more context from that data, and then make it available in a pre-trained model for others to use in their own domain specific applications.

4. The Transformer architecture

If you take one thing away from this post, make it a general understanding of the Transformer architecture and how it relates to models like BERT and GPT-3. This will let you look at different Transformer models, understand what tweaks they made to the vanilla architecture, and see what problem or task they're trying to address. This provides a key insight into what task or domain it might be better suited to.

As we learned, the original Transformer paper was called " Attention is all you need ". The name itself is important since it points to how it deviates from previous approaches. In section 2, we noted the models like ELMo employed RNNs to process text sequentially in a loop-like approach.

Now think of a simple sentence, like " The cat ran away when the dog chased it down the street". For a person, this is an easy sentence to comprehend, but there's actually a number of difficulties if you think of processing this sequentially. Once you get to the " it" part, how do you know what it refers to? You could have to store some state to identify that the key protagonist in this sentence is the " cat". Then, you'd have to find some way to relate the " it" to the " cat " as you continue to read the sentence.

Now imagine that the sentence could be any number of words in length, and try to think about how you would keep track of what's being referred to as you process more and more text.

This is the problem sequential models ran into.

They were limited. They could only prioritise the importance of words that were most recently processed. As they continued to move along the sentence, the importance or relevance of previous words started to diminish.

Think of it like adding information to a list as you process each new word. The more words you process, the more difficult it is to refer to words at the start of the list. Essentially, you need to move back, one element at a time, word by word until you get to the earlier words and then see if those entities are related.

Does the " it" refer to the " cat"? This is known as the " Vanishing Gradient " problem, and ELMo used special networks known as Long Short-Term Memory Networks (LSTMs) to alleviate the consequences of this phenomenon. LSTMs did address this issue, but they didn't eliminate it.

Ultimately, they couldn't create an efficient way to "focus" on the important word in each sentence. This is the problem the Transformer network addressed by using the mechanism we already know as "attention".

The "Attention is all you need" paper used attention to improve the performance of machine translation. They created a model with two main parts:

1. Encoder: This part of the "Attention is all you need" model processes the input text, looks for important parts, and creates an embedding for each word based on relevance to other words in the sentence.

2. Decoder: This takes the output of the encoder, which is an embedding, and then turns that embedding back into a text output, i.e. the translated version of the input text.

The key part of the paper is not, however, the encoder or the decoder, but the layers used to create them. Specifically, neither the encoder nor the decoder used any recurrence or looping, like traditional RNNs. Instead, they used layers of "attention" through which the information passes linearly. It didn't loop over the input multiple times - instead, the Transformer passes the input through multiple attention layers.

You can think of each attention layer as "learning" more about the input, i.e. looking at different parts of the sentence and trying to discover more semantic or syntactic information. This is important in the context of the vanishing gradient problem we noted earlier.

As sentence length increases, it gets increasingly difficult for RNNs to process them and learn more information. Every new word means more data to store, and makes it harder to retrieve that to understand the context in the sentence.

The Transformer can address this by simply adding more "attention heads", or layers. Since there's no looping, it doesn't hit the vanishing gradient problem. The Transformer does still have issues with processing longer text, but it's different from the RNN problem and not something we need to get into here. For comparison, the largest BERT model consists of 24 attention layers. GPT-2 has 12 attention layers and GPT-3 has 96 attention layers.

We won't get into the fine details of how attention works here. We can look at it in another post. In the meantime, you can checkout the blog posts linked in the above diagrams. They're excellent resources on attention and how it works. The important point in this post is to understand that attention is how the Transformer architecture eliminates many of the issues we encounter when we use RNNs for NLP. The other issue we noted earlier, when looking at the limits of RNNs, was the ability to process text in a non-sequential manner. The Transformer, via the attention mechanism, enables these models to do precisely that and process text bidirectionally.

5. The importance of bidirectionality

We noted earlier that RNNs were the architectures used to process text prior to the Transformer. RNNs use recurrence or looping to be able to process sequences of textual input. Processing text in this way creates two problems:

1. Its slow: Processing text sequentially, in one direction, is costly since it creates a bottleneck. It's like a single lane road during peak time where there are long tailbacks, versus the road with few cars on it off peak. We know that generally speaking, these models perform better if they're trained on more data, so this bottleneck was a big problem if we wanted better models.

2. It misses key information: We know that humans don't read text in an absolutely pure sequential manner. As psychologist Daniel Willingham notes in his book "The Reading Mind", "we don't read letter by letter, we read in letter clumps, figuring out a few letters at a time". The reason is we need to know a little about what is ahead to understand what we're reading now. The same is true for NLP language models. Processing text in one direction limits their ability to learn from data

We saw that ELMo attempted to address this via a method we referred to as "shallow" bidirectionality. It processed the text in one direction, then reversed the text, i.e. started from the end, and processed the text in that way. By concatenating both these embeddings, the hope was that this would help capture the different meaning in sentences like:

The " mouse" in both these sentences refers to a very different entity depending on whether the last word in the sentence is " laptop" or " cat". By reversing the sentence and starting with the word " cat", ELMo attempts to learn the context so that it can encode the different meaning of the word " mouse ". By processing the word "cat" first, ELMo is able to incorporate the different meaning into the "reversed" embedding. This is how ELMo was able to improve on traditional, static, Word2Vec embeddings which could only encode one meaning per word.

Without understanding the nuance involved in how this process works, we can see that the ELMo approach is not ideal.

We would prefer a mechanism which enables the model to look at the other words in the sentence during the encoding, so it can know whether or not we should be worried that there is a mouse on the table! And this is exactly what the attention mechanism of the Transformer architecture enables these models to do.

This example from the Google blog shows how the attention mechanism in the Transformer network can "focus" attention on what "it" refers to, in this case the street, while also recognising the importance of the word "animal", i.e. the animal did not cross it, the street, since it was too wide.
Being able to read text bidirectionally is one of the key reasons that Transformer models like BERT can achieve such impressive results in traditional NLP tasks. As we see from the above example, being able to know what "it" refers to is difficult when you read text in only one direction, and have to store all the state sequentially.

I guess it's no surprise that this is a key feature of BERT, since the B in BERT stands for "Bidirectional". The attention mechanism of the Transformer architecture allows models like BERT to process text bidirectionally by:

As a result of these factors, being able to process text in parallel, embedding the position of the input in the embedding and enabling easy lookup of each input, models like BERT can "read" text bidirectionally.

Technically it's not bidirectional, since these models are really looking at all the text at once, so it's non-directional. But it's better to understand it as a way to try and process text bidirectionally to improve the model's ability to learn from the input.

Being able to process text this way introduces some problems that BERT needed to address with a clever technique called "masking", which we'll discuss in section 8. But, now that we understand a little bit more about the Transformer architecture, we can look at the differences between BERT and the vanilla Transformer architecture in the original "Attention is all you need" paper.

6. How are BERT and the Transformer different?
The Transformer network as described in the "Attention is all you need" paper. Notice that it has both and encoder, on the left, and decoder, on the right, which make us the network. | Source: Attention is all you need
When you read about the latest models, you'll see them called "Transformer" models. Sometimes the word will be used loosely, and models like BERT and GPT-3 will both be referred to as "Transformer" models. But, these models are very different in some important ways.

Understanding these differences will help you know which model to use for your own unique use case. The key to understanding the different models is knowing how and why they deviate from the original Transformer architecture. In general, the main things to look out for are:

Now that we know what to look for, how does BERT differ from the vanilla Transformer?

Now that we know how BERT differs from the vanilla Transformer architecture, we can look closely at these parts of the BERT model. But first, we need to understand how BERT "reads" text.

7. Tokenizers: how BERT reads

When we think about models like BERT, we often overlook one important part of the process: how do these models "read" the input to be able to learn from the vast amounts of text they're trained on?

You might think this is the easy part. Just process each word at a time, identify each word by a space separating them, and then pass that to the attention layers to do their magic.

However, a few problems arise when we try and tokenize text via words or other simple methods such as punctuation, for example:

HuggingFace has a great tokenizer library. It includes great example tutorials on how the different approaches work. Here, for example, it shows how the base vocabulary is created based on the frequency of words in the training data. This shows how a word like "hug" ends up being tokenized as "hug", while "pug" is tokenized by two subword parts, "p" and "ug".

How did BERT solve these problems? It implemented a new approach to tokenization, WordPiece, which applied a sub-word approach to tokenization. WordPiece solves many of the problems previously associated with tokenization by:

Using sub-words instead of words: Instead of looking at whole words, WordPiece breaks words down into smaller parts, or building blocks of words, so that it can use them to build up different words. For example, think of a word like "learning". You could break that into three parts, "lea", "rn" and "ing". This way, you can make a wide range of words using the building blocks:

Learning = lea + rn + ing

Learn = lea + rn

You can then combine these together with other subword units to make other words:

You can create whole words for your most common terms, but then create subword for the other words that don't appear as often. Then you can be confident that you'll tokenize any word by using the building blocks you've created. If you encounter a completely new word, you can always piece it together character by character, e.g. LOL = l + o + l, since the subword token library also includes each single character. You can build up any word even if you've never seen it. But generally, you should have some subword unit you can use and add on a few characters as needed.

1. Creating a small vocabulary size: Since we don't need a token for each word, we can instead create a relatively small vocabulary. BERT for example uses about 30,000 tokens in its library. That may seem large, but think about every possible word ever invented and used, and all the different ways they can be used. To cover that, you'd need vocabularies with millions of tokens, and you still wouldn't cover everything. Being able to do this with 30,000 tokens is incredible. It also means we don't need to use the <UNK> token to still have a small model size and train on large amounts of data.

2. It still assumes words are separated by spaces, but ... While WordPiece does assume that words are separated by spaces, new subword token libraries such as SentecePiece (used with the Transformer based model) or the Multilingual Universal Sentence Encoder (MUSE), are further enhancements of this subword approach and the most common libraries used for new models.

I wrote an in-depth review of tokenizers here if you want to dive deeper into the subject.

8. Masking: hard word versus smart work

As we noted in section 6, when you're looking at different Transformer based models, it can be interesting to look at how they differ in their approach to training. In the case of BERT, the training approach is one of the most innovative aspects. Transformer models offer enough improvements just with the vanilla architecture that you can just train them using the traditional language model approach and see massive benefits.

This is where you predict future tokens from previous tokens. The previous RNNs used autoregressive techniques to train their models. GPT models similarly use an autoregressive approach to training their models. Except with the Transformer architecture, (i.e. the decoder as we noted earlier), they can train on more data than ever before. And, as we now know, the models can learn context better via the attention mechanism and the ability to process input bidirectionally.

Instead, BERT used an innovative technique to try and "force" the models to learn more from given data. This technique also raises a number of interesting aspects about how deep learning models interact with a given training technique:

1. Why is masking needed? Remember that the Transformer allows models to process text bidirectionally. Essentially, the model can see all the words in the input sequence at the same time. Previously, with RNN models, they only saw the current work in the input and had no idea what was the next word. Getting those models to predict the next word was easy, the model didn't know it so had to try and predict it and learn from that. However, with the Transformer, BERT can "cheat" and look at the next word so it will learn nothing. It's like taking a test and being given the answers. You won't study if you know the answer will always be there. BERT uses masking to solve this.

2. What is masking? Masking (also known as a cloze test) simply means that instead of predicting the next word, we hide or "mask" a word, and then force the model to predict that word. For BERT, 15% of the input tokens are masked before the model gets to see them, so there's no way for it to cheat. To do this, a word is randomly selected and simply replaced with a "[MASK]" token, and then fed into the model.

3. No labels needed: The other things to always remember for these tasks is that if you are designing a new training technique, it's best if you don't need to label or manually structure the data for training. Masking achieves this by requiring a simple approach to enable training on vast amounts of unstructured data. This means it can be trained in a fully unsupervised manner.

4. 80% of 15%: While masking might seem like a simple technique, it has a lot of nuance to it. Of the 15% of token selected for masking, only 80% are actually replaced with the mask token. Instead, 10% are replaced with a random word and 10% are replaced with the correct word.

5. What's interesting about masking? Why not just mask 100% of the 15% of inputs selected? That's an interesting question. If you do that, the model will know it only needs to predict the masked words, and learn nothing about other words in the input. Not great. You need the model to learn something about all the input, not just the 15% of masked inputs. To "force" the model to learn context for the non-masked words, we need to replace some of the tokens with a random word, and some with the correct word. This means BERT can never know if the unmasked word it's being asked to predict is the correct word. If, as an alternative approach, we used the MASK token 90% of the time and then used an incorrect word 10% of the time, BERT would know while predicting a non-masked token that it's always the wrong word. Similarly, if we used only the correct word 10% of the time, BERT would know it's always right, so it would just keep reusing the static word embedding it learned for that word. It would never learn a contextual embedding for that word. This is a fascinating insight into how Transformer models represent these states internally. No doubt we'll see more training tweaks like this pop up in other models.

9. BERT: fine-tune and transfer learning

As should be clear by now, BERT broke the mold in more ways than one when it was published a few years ago. One other difference was the ability to be tuned to a specific domain. This builds on many of the things we've already discussed, such as being a pre-trained model that meant people didn't need access to a large dataset to train it from scratch. You can build on models that have learned from a much larger dataset, and "transfer" that knowledge to your model for a specific task or domain.

The fact that BERT is a pre-trained model means we can fine-tune it to our domain, because:

1. BERT can perform transfer learning: Transfer learning is a power concept that was first implemented for machine vision. Models trained on ImageNet were then available for other "downstream" tasks, where they could build on the knowledge of the model trained on more data. In other words, these pre-trained models can "transfer" the knowledge they learned on the large dataset to another model, which needs much less data to perform well at a specific task. For machine vision, the pre-trained models know how to identify general aspects of images, such as lines, edges, the outlines of faces, the different objects in a picture and so on. They don't know fine-grained detail like individual facial differences. A small model can be easily trained to transfer this knowledge to its tasks, and recognize faces or objects specific to its task. If you wanted to identify a diseased plant, you didn't need to start from scratch.

2. You can choose relevant layers to tune: Although it's still a matter of ongoing research, it appears that higher layers of the BERT models learn more contextual or semantic knowledge while lower levels tend to perform better on syntactic related tasks. The higher layers are generally more related to task specific knowledge. For fine-tuning, you can add your own layer on top of BERT, and use a small amount of data to train that on some task, like classification. In those cases, you'd freeze the parameters of the later layer, and only allow your added layer parameters to change. Alternatively, you can "unfreeze" these higher layers and fine-tune BERT by letting these values change.

3. BERT needs much less data: Since it appears that BERT has already learned some "general" knowledge about language, you need much less data to fine-tune it. This means you can use labelled data to train a classifier, but you need to label much less data, or you can use the original training technique of BERT, like NSP, to train it on unlabelled data. If you have 3000 sentence pairs, you can fine-tune BERT with your unlabelled data.

All this means that BERT, and many other Transformer models, can be easily tuned to your business domain. While there do seem to be some issues with fine-tuning, and many models have trained BERT from scratch for unique domains like Covid19 information retrieval, it's still a big shift in the design of these models that they're capable of being trained on unsupervised and relatively small amounts of data.

10. Avocado chairs: What's next for BERT and the Transformer architecture

Now that we've come to the end of our whirlwind review of BERT and the Transformer architecture, let's look forward to what we can expect in the future for this exciting area of Deep Learning:

1. Questions about limits of these models: There is a fascinating, almost philosophical, discussion around the potential limits of trying to extract meaning from text alone. Some recently published papers show how there is a new form of thinking about what deep learning models can learn from language. Are these models doomed to hit a point of diminishing returns in terms of training on more and more data? These are exciting questions, which are moving outside of the boundaries of NLP into the broader domain of general AI. For more on the limits of these models, see our previous neptune.ai post on this topic.

2. Interpretability of models: It's often said that we start to use a technology, and only later on figure out how it works. For example, we learned to fly planes before we really understood everything about turbulence and aerodynamics. Similarly, we're using these models and we really don't understand what or how they're learning. For some of this work, I strongly recommend Lena Voita's blog. Her research on BERT, GPT and other Transformer models is incredible, and an example of the growing body of work where we're starting to reverse-engineer how these models are working.

3. An image is worth a thousand words: One interesting potential development of the Transformer models is that there's some work already underway which combines text and vision to enable models to generate images from sentence prompts. Similarly, Convolution Neural Networks (CNNs) were the core technology of machine vision until recently, when Transformer based models started being used in this field. This is an exciting development, since the combination of vision and text has the potential to improve the performance of these models rather than using text alone. As we noted earlier, what started out as a machine translation model has morphed into a technology that seems closer than any other area of AI to realise the holy grail of general intelligence. Watch this space closely, it might just be the most exciting area of AI at the moment.

Conclusion

And that's it. If you made it this far - thank you so much for reading! I hope this article helped you understand BERT and the Transformer architecture.

It's truly one of the most interesting domains in AI right now, so I encourage you to keep on exploring and learning about this. You can start with the various different links that I left throughout this article.

Good luck on your AI journey!

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10 Things You Need to Know About BERT and the Transformer Architecture That Are Reshaping the AI Landscape - neptune.ai

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Few areas of AI are more exciting than NLP right now. In recent years language models (LM), which can perform human-like linguistic tasks, have evolved to perform better than anyone could have expected. In fact, they're performing so well that people are wondering whether they're reaching a level of general intelligence, or the evaluation metrics...

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An enormous hourglass with multi-coloured sand flowing from top to bottom - Midjourney v4
There has been a large amount of attention focused on ChatGPT and before that, DALL-E-2, but its important to understand that both of these are based on the same Generative Pre-Trained Transformer language model called GPT-3. Before we can understand where all this is leading us in the future, it is useful to understand what GPT-3 represents, and how rapidly we arrived at where we are today.
What is a GPT ?

A Generative Pre-Trained Transformer (GPT) is a type of language model developed by OpenAI. It uses a transformer neural network architecture and is pre-trained on a large corpus of text data. The pre-training allows the model to learn general language patterns and representations, which can then be fine-tuned for a variety of natural language processing tasks, such as language translation, text summarization, and question answering.

The GPT models are generative, meaning they can generate text that is similar to the text in the training data. But a GPT like GPT-3 is capable of more than generating text. Due to their transformer architecture and pre-training on a diverse text corpus, GPT models have the ability to generate not just text but also other types of data such as images and audio, given the right fine-tuning and the input format.

For example, GPT-3 has been fine-tuned to generate images, music, code, and even perform simple tasks like scheduling and translation, this is possible due to the large amount of knowledge it has been pre-trained on, but it still requires fine-tuning on specific task and/or input format.

2017 - Pre GPT-1

There were earlier examples of models that used similar concepts to GPT-1 before it was introduced, but GPT-1 was the first model to be called GPT and introduced by OpenAI.

For example, the Transformer model was introduced by Google in 2017. The Transformer model is the architecture used in GPT models and it introduced the attention mechanism that GPT models use to understand the relationships between words in a sentence.

Additionally, other models such as ELMO and BERT, introduced in 2018 by Allen Institute for Artificial Intelligence, also used the concept of pre-training on a large dataset and fine-tuning on specific tasks, which is a key aspect of GPT models.

2018 - GPT-1

GPT-1 was the first version of the GPT series of language models developed by OpenAI. Released in 2018, it was trained on a dataset of about 40GB of text data and have 1.5 billion parameters.

It was capable of generating human-like text, completing tasks such as translation, summarization, and question answering with a high degree of accuracy.June 2019 GPT-2

June 2019 - GPT-2

GPT-2 is a more advanced version of GPT-1, and it has several key differences and improvements over its predecessor. Some of the main differences between GPT-2 and GPT-1 include:

GPT-2 was trained on a much larger dataset than GPT-1, with about 570GB of text data. This allows GPT-2 to have a more diverse and comprehensive understanding of natural language.

GPT-2 has significantly more parameters than GPT-1, with 1.5 billion parameters. This allows GPT-2 to have a more complex and powerful model, which is better able to generate more human-like text.

GPT-2 is able to generate more coherent and fluent text than GPT-1. GPT-2 is also able to generate longer paragraphs of text, which makes it more useful for tasks such as writing essays and articles.

GPT-2 has a better understanding of natural language than GPT-1, it is better able to answer questions, summarize text, translate text and do many other natural language processing tasks with a high degree of accuracy.

GPT-2 can be fine-tuned to specific tasks more easily than GPT-1, which allows it to achieve better performance on a wide range of natural language processing tasks.

June 2020 - GPT-3

GPT-3 is the most recent and advanced version of the GPT series of language models developed by OpenAI, and it has several key differences and improvements over its predecessor GPT-2.

GPT-3 was trained on a much larger dataset than GPT-2, with about 570GB of text data. This allows GPT-3 to have a more diverse and comprehensive understanding of natural language.

GPT-3 has significantly more parameters than GPT-2, with 175 billion parameters. This allows GPT-3 to have a more complex and powerful model, which is better able to generate more human-like text.

GPT-3 is able to generate more coherent and fluent text than GPT-2, and is able to perform a wide range of natural language processing tasks such as writing essays, articles, poetry and even coding with a high degree of accuracy.

GPT-3 has a better understanding of natural language than GPT-2, it is better able to answer questions, summarize text, translate text and do many other natural language processing tasks with a high degree of accuracy.

GPT-3 can be fine-tuned to specific tasks more easily than GPT-2, which allows it to achieve better performance on a wide range of natural language processing tasks.

GPT-3 can perform tasks that involve multiple modalities, such as image and text understanding, image captioning, and even generating images from text.

It is perhaps the multi-modal capabilities of GPT-3 that distinguish it the most, because it allowed the development of DALL-E-2. Prior to DALL-E-2, all of the capabilities described could only be accessed through APIs. DALL-E-2 captured the imagination of a much wider audience.
The arrival of DALL-E-2 and ChatGPT did more to democratize the use of AI than anything that has gone on before. For the first time, everyone from students, to artists and business people could make use of the GPT-3 capabilities.
January 2021 - DALL-E-2

DALL-E 2 was released by OpenAI on January 12, 2021. DALL-E 2 is the second version of DALL-E, which is a model for generating images from text descriptions. DALL-E 2 builds on the capabilities of the first version of DALL-E, which was released in December 2020, and it is able to generate a wider variety of images, including ones that are more abstract and less predictable

This model is considered as a major advancement in the field of image generation and AI, it can generate images that are realistic and diverse, and it's able to generate images of things that do not exist in the real world.

Finally, ChatGPT was introduced and now everyone could easily interact with the GPT-3 model.

November 2022 - ChatGPT

ChatGPT is a variant of the GPT-3 model that was fine-tuned for conversational language understanding and generation. Like, DALL-E-2, ChatGPT is not a standalone model, it is built on top of the GPT-3 model.

ChatGPT is a chatbot, but unlike most chatbots, ChatGPT remembers previous parts of the conversation. In this manner, it mimics a human conversation.
So, why is this time-line important to understand ? In 2016, an AI computer program called AlphaGo defeated Lee Sedol, a professional Go player, who at the time was considered to be one of the best players in the world. It was clear at the time that machine learning based AI, was going to outperform humans but only in very specialized areas. A year later, OpenAI introduced the first GPT, and what many thought would take a decade or more has happened in the last 5 years. What we are seeing now, at the end of 2022 are more generalized AI capabilities, a long way from AGI, but a step closer. Its only the beginning, and the pace at which change is happening is likely to accelerate not slow down.

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ChatGPT - How we got to where we are today - a timeline of GPT development

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There has been a large amount of attention focused on ChatGPT and before that, DALL-E-2, but its important to understand that both of these are based on the same Generative Pre-Trained Transformer language model called GPT-3.

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This semester at New York Tech, we have witnessed two significant developments: a new Office of Diversity, Equity, Inclusion & Belonging and the release of ChatGPT, game-changing AI writing assistive technology that has resulted in headlines such as " The College Essay Is Dead."

How do these two developments connect?

They encourage us to invite students into the community by providing opportunities to engage with local problems and knowledge. We can meet this challenge with authentic assessment that requires higher order thinking, such as synthesis, evaluation, reflection, and application to real world contexts. This approach also values the experience students bring to the classroom, another significant component of inclusive learning.

To illustrate, I gave ChatGPT the type of prompt that may show up in first-year writing classes. The response below would provide a solid framework for the average college essay, but one that students can also complete with information readily available on the internet. If AI can respond well to a prompt, we have to ask ourselves how valuable this broad approach really is to college-level learning.

Then I asked ChatGPT a question about local conditions that exist within our own New York Tech community. The response below indicates the limits of AI innovation and the opportunity for students to develop a sense of belonging by researching, understanding, and sharing the implications of key problems for people in their daily lives.

Our Center for Teaching and Learning has reminded us before about the power of authentic assessment. We will need our creativity and ability to apply the principles of authentic assessment to meet the challenge. Navin Pokala, Associate Professor of Biological and Chemical Sciences, clarifies here: "Text generating programs have been available for years (for example, the Postmodern Generator). However, the sheer power and flexibility of ChatGPT combined with the simplicity of the interface dramatically increases the challenges of ensuring academic integrity of take-home assignments."

Below are some resources and tips that address this moment. Ultimately, it will be exciting to explore how we can seize this chance to make our assessments more meaningful to students and to foster a sense of belonging to the New York Tech community.

Jonathan Goldman, Professor of Humanities and writing faculty member, offers a suggested assignment prompt that incorporates the AI technology with a valuable metacognitive process: "Read the prompt and brainstorm how you would answer in an essay. Feed the prompt to an AI text generator. Read the resulting essay and find specific places where you disagree with the way it answers the question. Write an essay critiquing the AI-generated essay. (Optional: have the AI text generator create an essay that critiques the essay created in (2), then critique that. And so on.)"

Fran Glazer posed a couple of ways these strategies might work in other disciplines: An architecture course might give students the plans for a specific structure and ask them to propose ways to make it more sustainable. A lab course might ask students to analyze the results of their own experiments and propose avenues of future study. And a marketing class might have students critique a marketing plan created by the AI text generator.

Amanda Golden, Associate Professor of Humanities and Writing Center Coordinator, recommends Emory professor and digital humanities expert Lauren Klein's response: "There's always been this concern that technologies will do away with what people do best, and the reality is that people have had to learn how to use these technologies to enhance what they do best."

Resources:

Contributor:
Jennifer Griffiths, Ph.D.
Professor, Humanities
Director of Writing
New York Institute of Technology
jgriff02@nyit.edu

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This semester at New York Tech, we have witnessed two significant developments: a new Office of Diversity, Equity, Inclusion & Belonging and the release of ChatGPT, game-changing AI writing assistive technology that has resulted in headlines such as " The College Essay Is Dead." How do these two developments connect?

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"The" ChatGPT link. Ignore the 'Access denied' pic.

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ChatGPT helps you get answers, find inspiration, and be more productive.

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AI powered tools for content creation, marketing, copywriting, image generation, video editing, developers, business, education and more.

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Proposals for citation of ChatGPT

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Collection of prompt examples

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Even if you aren't into artificial intelligence, it's time to pay attention to ChatGPT, because this one is a big deal.

The tool, from a power player in artificial intelligence called OpenAI, lets you type natural-language prompts. ChatGPT then offers conversational, if somewhat stilted, responses. The bot remembers the thread of your dialogue, using previous questions and answers to inform its next responses. It derives its answers from huge volumes of information on the internet.

ChatGPT is a big deal. The tool seems pretty knowledgeable in areas where there's good training data for it to learn from. It's not omniscient or smart enough to replace all humans yet, but it can be creative, and its answers can sound downright authoritative. A few days after its launch, more than a million people were trying out ChatGPT.

But be careful, OpenAI warns. ChatGPT has all kinds of potential pitfalls, some easy to spot and some more subtle.

"It's a mistake to be relying on it for anything important right now," OpenAI Chief Executive Sam Altman tweeted. "We have lots of work to do on robustness and truthfulness." Here's a look at why ChatGPT is important and what's going on with it.

And it's becoming big business. In January, Microsoft pledged to invest billions of dollars into OpenAI. A modified version of the technology behind ChatGPT is now powering Microsoft's new Bing challenge to Google search and, eventually, it'll power the company's effort to build new AI co-pilot smarts in to every part of your digital life.

Bing uses OpenAI technology to process search queries, compile results from different sources, summarize documents, generate travel itineraries, answer questions and generally just chat with humans. That's a potential revolution for search engines, but it's been plagued with problems like factual errors and and unhinged conversations.

What is ChatGPT?

ChatGPT is an AI chatbot system that OpenAI released in November to show off and test what a very large, powerful AI system can accomplish. You can ask it countless questions and often will get an answer that's useful.

For example, you can ask it encyclopedia questions like, "Explain Newton's laws of motion." You can tell it, "Write me a poem," and when it does, say, "Now make it more exciting." You ask it to write a computer program that'll show you all the different ways you can arrange the letters of a word.

Here's the catch: ChatGPT doesn't exactly know anything. It's an AI that's trained to recognize patterns in vast swaths of text harvested from the internet, then further trained with human assistance to deliver more useful, better dialog. The answers you get may sound plausible and even authoritative, but they might well be entirely wrong, as OpenAI warns.

Chatbots have been of interest for years to companies looking for ways to help customers get what they need and to AI researchers trying to tackle the Turing Test. That's the famous "Imitation Game" that computer scientist Alan Turing proposed in 1950 as a way to gauge intelligence: Can a human conversing with a human and with a computer tell which is which?

But chatbots have a lot of baggage, as companies have tried with limited success to use them instead of humans to handle customer service work. A study of 1,700 Americans, sponsored by a company called Ujet, whose technology handles customer contacts, found that 72% of people found chatbots to be a waste of time.

ChatGPT has rapidly become a widely used tool on the internet. UBS analyst Lloyd Walmsley estimated in February that ChatGPT had reached 100 million monthly users the previous month, accomplishing in two months what took TikTok about nine months and Instagram two and a half years. The New York Times, citing internal sources, said 30 million people use ChatGPT daily.

What kinds of questions can you ask?

You can ask anything, though you might not get an answer. OpenAI suggests a few categories, like explaining physics, asking for birthday party ideas and getting programming help.

I asked it to write a poem, and it did, though I don't think any literature experts would be impressed. I then asked it to make it more exciting, and lo, ChatGPT pumped it up with words like battlefield, adrenaline, thunder and adventure.

One wacky example shows how ChatGPT is willing to just go for it in domains where people would fear to tread: a command to write " a folk song about writing a rust program and fighting with lifetime errors."

ChatGPT's expertise is broad, and its ability to follow a conversation is notable. When I asked it for words that rhymed with "purple," it offered a few suggestions, then when I followed up "How about with pink?" it didn't miss a beat. (Also, there are a lot more good rhymes for "pink.")

When I asked, "Is it easier to get a date by being sensitive or being tough?" GPT responded, in part, "Some people may find a sensitive person more attractive and appealing, while others may be drawn to a tough and assertive individual. In general, being genuine and authentic in your interactions with others is likely to be more effective in getting a date than trying to fit a certain mold or persona."

You don't have to look far to find accounts of the bot blowing people's minds. Twitter is awash with users displaying the AI's prowess at generating art prompts and writing code. Some have even proclaimed "Google is dead," along with the college essay. We'll talk more about that below.

CNET writer David Lumb has put together a list of some useful ways ChatGPT can help, but more keep cropping up. One doctor says he's used it to persuade a health insurance company to pay for a patient's procedure.

Who built ChatGPT and how does it work?

ChatGPT is the brainchild of OpenAI, an artificial intelligence research company. Its mission is to develop a "safe and beneficial" artificial general intelligence system or to help others do so. OpenAI has 375 employees, Altman tweeted in January. "OpenAI has managed to pull together the most talent-dense researchers and engineers in the field of AI," he also said in a January talk.

It's made splashes before, first with GPT-3, which can generate text that can sound like a human wrote it, and then with DALL-E, which creates what's now called "generative art" based on text prompts you type in.

GPT-3, and the GPT 3.5 update on which ChatGPT is based, are examples of AI technology called large language models. They're trained to create text based on what they've seen, and they can be trained automatically - typically with huge quantities of computer power over a period of weeks. For example, the training process can find a random paragraph of text, delete a few words, ask the AI to fill in the blanks, compare the result to the original and then reward the AI system for coming as close as possible. Repeating over and over can lead to a sophisticated ability to generate text.

It's not totally automated. Humans evaluate ChatGPT's initial results in a process called finetuning. Human reviewers apply guidelines that OpenAI's models then generalize from. In addition, OpenAI used a Kenyan firm that paid people up to $3.74 per hour to review thousands of snippets of text for problems like violence, sexual abuse and hate speech, Time reported, and that data was built into a new AI component designed to screen such materials from ChatGPT answers and OpenAI training data.

ChatGPT doesn't actually know anything the way you do. It's just able to take a prompt, find relevant information in its oceans of training data, and convert that into plausible-sounding paragraphs of text. "We are a long way away from the self-awareness we want," said computer scientist and internet pioneer Vint Cerf of the large language model technology ChatGPT and its competitors use.

Is ChatGPT free?

Yes, for the moment at least, but in January OpenAI added a paid version that responds faster and keeps working even during peak usage times when others get messages saying, "ChatGPT is at capacity right now."

You can sign up on a waiting list if you're interested. OpenAI's Altman warned that ChatGPT's "compute costs are eye-watering" at a few cents per response, Altman estimated. OpenAI charges for DALL-E art once you exceed a basic free level of usage.

But OpenAI seems to have found some customers, likely for its GPT tools. It's told potential investors that it expects $200 million in revenue in 2023 and $1 billion in 2024, according to Reuters.

What are the limits of ChatGPT?

As OpenAI emphasizes, ChatGPT can give you wrong answers and can give "a misleading impression of greatness," Altman said. Sometimes, helpfully, it'll specifically warn you of its own shortcomings. For example, when I asked it who wrote the phrase "the squirming facts exceed the squamous mind," ChatGPT replied, "I'm sorry, but I am not able to browse the internet or access any external information beyond what I was trained on." (The phrase is from Wallace Stevens' 1942 poem Connoisseur of Chaos.)

ChatGPT was willing to take a stab at the meaning of that expression once I typed it in directly, though: "a situation in which the facts or information at hand are difficult to process or understand." It sandwiched that interpretation between cautions that it's hard to judge without more context and that it's just one possible interpretation.

ChatGPT's answers can look authoritative but be wrong.

"If you ask it a very well structured question, with the intent that it gives you the right answer, you'll probably get the right answer," said Mike Krause, data science director at a different AI company, Beyond Limits. "It'll be well articulated and sound like it came from some professor at Harvard. But if you throw it a curveball, you'll get nonsense."

The journal Science banned ChatGPT text in January. "An AI program cannot be an author. A violation of these policies will constitute scientific misconduct no different from altered images or plagiarism of existing works," Editor in Chief H. Holden Thorp said.

The software developer site StackOverflow banned ChatGPT answers to programming questions. Administrators cautioned, "because the average rate of getting correct answers from ChatGPT is too low, the posting of answers created by ChatGPT is substantially harmful to the site and to users who are asking or looking for correct answers."

You can see for yourself how artful a BS artist ChatGPT can be by asking the same question multiple times. I asked twice whether Moore's Law, which tracks the computer chip industry's progress increasing the number of data-processing transistors, is running out of steam, and I got two different answers. One pointed optimistically to continued progress, while the other pointed more grimly to the slowdown and the belief "that Moore's Law may be reaching its limits."

Both ideas are common in the computer industry itself, so this ambiguous stance perhaps reflects what human experts believe.

With other questions that don't have clear answers, ChatGPT often won't be pinned down.

The fact that it offers an answer at all, though, is a notable development in computing. Computers are famously literal, refusing to work unless you follow exact syntax and interface requirements. Large language models are revealing a more human-friendly style of interaction, not to mention an ability to generate answers that are somewhere between copying and creativity.

Will ChatGPT help students cheat better?

Yes, but as with many other technology developments, it's not a simple black-and-white situation. Decades ago, students could copy encyclopedia entries and use calculators, and more recently, they've been able to use search engines and Wikipedia. ChatGPT offers new abilities for everything from helping with research to doing your homework for you outright. Many ChatGPT answers already sound like student essays, though often with a tone that's stuffier and more pedantic than a writer might prefer.

Google programmer Kenneth Goodman tried ChatGPT on a number of exams. It scored 70% on the United States Medical Licensing Examination, 70% on a bar exam for lawyers, nine out of 15 correct on another legal test, the Multistate Professional Responsibility Examination, 78% on New York state's high school chemistry exam's multiple choice section, and ranked in the 40th percentile on the Law School Admission Test.

High school teacher Daniel Herman concluded ChatGPT already writes better than most students today. He's torn between admiring ChatGPT's potential usefulness and fearing its harm to human learning: "Is this moment more like the invention of the calculator, saving me from the tedium of long division, or more like the invention of the player piano, robbing us of what can be communicated only through human emotion?"

Dustin York, an associate professor of communication at Maryville University, hopes educators will learn to use ChatGPT as a tool and realize it can help students think critically.

"Educators thought that Google, Wikipedia, and the internet itself would ruin education, but they did not," York said. "What worries me most are educators who may actively try to discourage the acknowledgment of AI like ChatGPT. It's a tool, not a villain."

Can teachers spot ChatGPT use?

Not with 100% certainty, but there's technology to spot AI help. The companies that sell tools to high schools and universities to detect plagiarism are now expanding to detecting AI, too.

One, Coalition Technologies, offers an AI content detector on its website. Another, Copyleaks, released a free Chrome extension designed to spot ChatGPT-generated text with a technology that's 99% accurate, CEO Alon Yamin said. But it's a "never-ending cat and mouse game" to try to catch new techniques to thwart the detectors, he said.

Copyleaks performed an early test of student assignments uploaded to its system by schools. "Around 10% of student assignments submitted to our system include at least some level of AI-created content," Yamin said.

OpenAI launched its own detector for AI-written text in February. But one plagiarism detecting company, CrossPlag, said it spotted only two of 10 AI-generated passages in its test. "While detection tools will be essential, they are not infallible," the company said.

Researchers at Pennsylvania State University studied the plagiarism issue using OpenAI's earlier GPT-2 language model. It's not as sophisticated as GPT-3.5, but its training data is available for closer scrutiny. The researchers found GPT-2 plagiarized information not just word for word at times, but also paraphrased passages and lifted ideas without citing its sources. "The language models committed all three types of plagiarism, and ... the larger the dataset and parameters used to train the model, the more often plagiarism occurred," the university said.

Can ChatGPT write software?

Yes, but with caveats. ChatGPT can retrace steps humans have taken, and it can generate actual programming code. "This is blowing my mind," said one programmer in February, showing on Imgur the sequence of prompts he used to write software for a car repair center. "This would've been an hour of work at least, and it took me less than 10 minutes."

You just have to make sure it's not bungling programming concepts or using software that doesn't work. The StackOverflow ban on ChatGPT-generated software is there for a reason.

But there's enough software on the web that ChatGPT really can work. One developer, Cobalt Robotics Chief Technology Officer Erik Schluntz, tweeted that ChatGPT provides useful enough advice that, over three days, he hadn't opened StackOverflow once to look for advice.

Another, Gabe Ragland of AI art site Lexica, used ChatGPT to write website code built with the React tool.

ChatGPT can parse regular expressions (regex), a powerful but complex system for spotting particular patterns, for example dates in a bunch of text or the name of a server in a website address. " It's like having a programming tutor on hand 24/7," tweeted programmer James Blackwell about ChatGPT's ability to explain regex.

Here's one impressive example of its technical chops: ChatGPT can emulate a Linux computer, delivering correct responses to command-line input.

What's off limits?

ChatGPT is designed to weed out "inappropriate" requests, a behavior in line with OpenAI's mission "to ensure that artificial general intelligence benefits all of humanity."

If you ask ChatGPT itself what's off limits, it'll tell you: any questions "that are discriminatory, offensive, or inappropriate. This includes questions that are racist, sexist, homophobic, transphobic, or otherwise discriminatory or hateful." Asking it to engage in illegal activities is also a no-no.

Even though OpenAI doesn't want ChatGPT used for malicious purposes, it's easy to use it to write phishing emails to try to fool people into parting with sensitive information, my colleague Bree Fowler reports. "The barrier to entry is getting lower and lower and lower to be hacked and to be phished. AI is just going to increase the volume," said Randy Lariar of cybersecurity company Optiv.

Is this better than Google search?

Asking a computer a question and getting an answer is useful, and often ChatGPT delivers the goods.

Google often supplies you with its suggested answers to questions and with links to websites that it thinks will be relevant. Often ChatGPT's answers far surpass what Google will suggest, so it's easy to imagine GPT-3 is a rival.

But you should think twice before trusting ChatGPT. As when using Google and other sources of information like Wikipedia, it's best practice to verify information from original sources before relying on it.

Vetting the veracity of ChatGPT answers takes some work because it just gives you some raw text with no links or citations. But it can be useful and in some cases thought provoking. You may not see something directly like ChatGPT in Google search results, but Google has built large language models of its own and uses AI extensively already in search.

That said, Google is keen to tout its deep AI expertise, ChatGPT triggered a "code red" emergency within Google, according to The New York Times, and drew Google co-founders Larry Page and Sergey Brin back into active work. Microsoft could build ChatGPT into its rival search engine, Bing. Clearly ChatGPT and other tools like it have a role to play when we're looking for information.

So ChatGPT, while imperfect, is doubtless showing the way toward our tech future.
Editors' note: CNET is using an AI engine to create some personal finance explainers that are edited and fact-checked by our editors. For more, see .

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Why We're Obsessed With the Mind-Blowing ChatGPT AI Chatbot

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Even if you aren't into artificial intelligence, it's time to pay attention to ChatGPT, because this one is a big deal. The tool, from a power player in artificial intelligence called OpenAI, lets you type natural-language prompts. ChatGPT then offers conversational, if somewhat stilted, responses.

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You can see where this is headed. A writing assignment asks students to compare and contrast feminist themes in Jane Eyre and Wuthering Heights. Yup, it can do that. A political science exam requires short-essay responses to questions around the rise and fall of the Soviet Union. Check.

Is the writing captivating? No. Is it coherent? Mostly. So what does this all mean for teaching? That's one question I set out to explore when I wrote about ChatGPT last month. I'd like to dig in here to some of the ideas that I heard from digital-literacy experts, writing instructors, and teaching and learning specialists.

First, if you want to make your assignments AI-proof, that's likely impossible. These tools can be used in large and small ways. Maybe you won't receive a paper written by a bot, but a bot-written essay may inform your students' writing. Yes, you can shift all writing to in-class assignments, or you can have students write by hand. But as Anna Mills, an English instructor at College of Marin pointed out to me, these strategies introduce new problems. For example, you may have students with learning disabilities who struggle under such conditions.

Second, you may want to shift to different types of assignments and assessments. Maybe you allow students to produce a podcast instead of writing a paper. Or you create fewer writing assignments, but build in more feedback and revision to the ones you keep. Or you try prompts whose answers are less likely to be found on the internet. Of course, there is always another option, which is to invest in detection software. Already several tools on the market promise to do that. But many digital-learning experts say that's a losing game - tech will keep advancing, and students will find ways around detection tools. Nor do most instructors want to become writing police.

The approach that most intrigued me is one that has to do with engaging students in a conversation about why and how they write, sometimes using these AI tools.

John Warner, a writing expert and author, notes that writing is a form of thinking. Writing requires you to process and synthesize a range of facts and ideas, and to come up with a coherent and hopefully insightful take on what you have learned. Students, though, may have been trained in high school to see writing as a form of regurgitation based on a set of formulas (compare and contrast!).

If you can explain to students the value of writing, and convince them that you are genuinely interested in their ideas, they are less likely to reach for the workaround, Warner told me.

There are also a host of people excited about using this technology in their classroom. Why? Well, for one, it's not going away. Ignoring the fact that students will use it is seen by some instructors as an abdication of professional responsibility. These are powerful tools and it's better to help students learn how to use them judiciously, and to understand their limitations and benefits. The other reason to use them is that they can help spark the creative process, professors say, and enhance learning.

Marc Watkins, an instructor at the University of Mississippi, wrote a thoughtful essay about this recently. He and his colleagues in the department of writing and rhetoric started a working group last summer to figure out how to incorporate AI research, writing, and brainstorming tools into their classes. They used a counterargument generator to encourage students to explore different perspectives on a topic, and a research tool to help them brainstorm.

"What message would we send our students," Watkins writes, "by using AI-powered detectors to curb their suspected use of an AI writing assistant, when future employers will likely want them to have a range of AI-related skills and competencies?

"What we should instead focus on is teaching our students data literacy so that they can use this technology to engage human creativity and thought."

The significance of that argument was brought home to me by Mills and others, when they compared these writing tools to calculators. There was also plenty of hand-wringing when hand-held calculators appeared, but now they're integral to teaching math and other STEM disciplines. Mills says she can even envision a day when ChatGPT is embedded in programs like Word and Google Docs.

Of course, all AI tools are not the same. Those that complete your sentences in an email are at one end of the spectrum, and large language models, like ChatGPT, are on the other. As Watkins and others point out, the most sophisticated tools are also potentially dangerous. Some have been shut down after turning out biased or nonsensical research papers that could still confuse a layperson. All the more reason, they say, to engage your students in a discussion of what they are, how they work, and how they can be used as an aide in learning.

These teaching experiments are coming fast and furious, but here is one that caught my eye. In this , Paul Fyfe, an associate professor of English at N.C. State University, described an experiment in which he asked students to "cheat" on a final essay using GPT-2, an earlier version of the AI that underlies ChatGPT, and then discuss how the AI influenced them, as well as its potential uses and abuses.

Have you experimented with any of these large language model tools in your teaching? What questions, concerns and hopes do you have about this technology? Write to me at beth.mcmurtrie@chronicle.com and your story may appear in a future newsletter.

Want More on AI?

Want to get involved in the conversation around AI writing tools? Here are a few resources you might find helpful.

Anna Mills has put together several documents:
AI Text Generators and Teaching Writing: Starting Points for Inquiry How do we prevent learning loss due to AI text generators? AI Text Generators: Sources to Stimulate Discussion among Teachers
Elsewhere, a group of professors is compiling examples of how instructors are using text generation technologies in their assignments. The results will be published in an open-access collection. You can find out more about the project on this site, Teaching with Text Generation Technologies.

If you're rather listen to a discussion, here are a couple of webinars:
AI and the Future of the Essay What might ChatGPT mean for higher education?
Finally, if you don't think the AI is sophisticated enough yet to be worth consideration, check these out:

An emeritus professor of educational technology created a 2,000-word academic paper in 10 minutes.

An admissions expert created a Common App essay that several admissions counselors thought was real.

A professor at the Wharton School a syllabus, an assignment and lecture notes for an MBA-level introductory course on entrepreneurship.

Keep on Teaching

New year, new webinar series for Teaching readers! This one, which we call Keep on Teaching, delves into two challenges we know many of you are contending with: how to make class time meaningful, and how to support students without overextending yourselves.

The first session, on January 20, is open to anyone. The second, on February 10, is just for newsletter subscribers. (If you haven't signed up yet, remember, it's free!)

In both sessions, you can expect an interactive discussion that brings together practical advice, evidence-based insights, encouragement, and a reminder that you're not alone.

Read more about the series and sign up here. We hope you'll join us!

Thanks for reading Teaching. If you have suggestions or ideas, please feel free to email us at beckie.supiano@chronicle.com or beth.mcmurtrie@chronicle.com.

- Beth

Learn more about our Teaching newsletter, including how to contact us, at the Teaching newsletter archive page.

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Instructors are already testing out these large language models. Students are, too.

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Incorporating ChatGPT in Philosophy Classes

By Cassie Finley, PhD Candidate in Philosophy, University of Iowa

Since the launch of ChatGPT on November 30th, there has been widespread discussion among educators about what this new technology means for education. OpenAI's ChatGPT is a large language model, which means that it uses massive amounts of existing written work to generate new writing that mirrors human patterns of language. The result is that a user can ask ChatGPT a question and ChatGPT produces a new, topical, grammatically correct, potentially accurate answer. So, for example, I asked ChatGPT in what ways large language models pose a challenge to education and received the following answer:

As ChatGPT said, the general ability to write like a human does not entail the ability to create accurate written work. Beyond this concern, though, within educational contexts the most immediate, obvious problem is that because ChatGPT actually creates new sentences, existing plagiarism detectors are useless for determining whether a piece of writing was written by a student or ChatGPT. This raises the practical concern that the majority of existing writing-based assignments, essays, and assessments done outside of class are vulnerable to outsourcing to this technology. However, this technology also raises more fundamental challenges regarding the purpose and value of assignments and assessments, given that seemingly many of these assignments can be outsourced to technology.

Accordingly, ChatGPT has elicited a bit of an existential crisis for education, causing educators to radically adjust course assignment structures. To address the practical concerns, some have proposed moving to oral or in-class exams (both of which seem to be particularly rare in philosophy classes), ultimately avoiding these technological problems by removing contexts wherein students could take advantage of the software. Others intend to use one of the various ChatGPT/AI-generated-text detectors, effectively as supplements to existing plagiarism detection tools, and simply expand course policies to specifically prohibit the use of text-generators.

However, these suggestions do not really engage at all with the fundamental, conceptual challenge to education by ChatGPT. What is the point of having students write something when ChatGPT could write it instead? One suggestion is to actively incorporate ChatGPT into the class structure, analogous to the ways that mathematics courses had to adjust to the invention (and subsequent significant advances) in calculators. For example, because ChatGPT is factually unreliable, instructors might have students ask ChatGPT a research question, then research and fact-check the answer using legitimate, reliable sources. ChatGPT also performs particularly poorly with evaluating its own answers, so I saw one suggestion that students should ask ChatGPT (what would otherwise be) exam questions, then grade and justify the answers based on course content and what the answer should have said. Similarly, someone had suggested in a Teaching Philosophy Facebook group that philosophy instructors use ChatGPT's answers to philosophical questions in class as a way to encourage students to critically evaluate the philosophical limitations of this new technology.

Following some of these suggestions for explicitly incorporating ChatGPT into the classroom, I tested it out in two of my philosophy courses. For context, the courses were once-weekly, one-hour, online introductory philosophy classes for students ages 10-13. Prior to the ChatGPT day, we had discussed a variety of philosophical topics including epistemology, personal identity, philosophy of mind, philosophy of time and time travel, ethics, metaethics, and friendship. Having already discussed multiple philosophical questions and potential answers with my students, we spent a class with ChatGPT shared on-screen for all to see, going around asking it various philosophical questions we had discussed within the context of the class already. The goal was to evaluate the responses given what the students had learned over the semester.

At first, the students were a mixture of excited, impressed, and deeply intimidated by the answers from ChatGPT. One student remarked that they didn't believe they could ever write anything as good as ChatGPT's answer! However, with a little reflection and guidance, they realized how often the answers failed to say much of anything. Many of the answers described what "some people may say," combined strings of weak claims without giving reason to support those claims, or failed to take substantive positions at all-all of which students were quick to realize are precisely some of the most important features of a good philosophical answer.

With a little guidance, students were identifying the problems and limitations of each answer, instead proposing alternative considerations that ChatGPT should have discussed. One question we asked and evaluated that was particularly demonstrative of this philosophical weakness was whether time travel is possible. ChatGPT answered with a few lines about how-"according to science"-time travel is not possible because we do not have the technology or scientific knowledge that would allow us to time travel. Having spent the semester emphasizing the importance of clarifying language in philosophy and a full class discussing David Lewis' " Paradoxes of Time Travel," the students were well-versed in how the answer depends on what you mean by "possible;" in one sense, the answer from ChatGPT was correct-as far as we know, it's not physically possible to travel through time with our current technology. Really, though, that's not an interesting answer to the question we were asking, since it might be equivalent to saying that at one time it was not possible for people to talk on the phone, since phones didn't exist. And yet, even attempting to rephrase the question was not going to lead to ChatGPT doing the conceptual work required for distinguishing different senses of "possibility" because it's precisely the philosophical creativity that considers the meaning rather than just the words themselves that large language models lack.

Because we had already talked about making conceptual distinctions like this, the students were able to apply their understanding of previous classes' discussions to evaluate and ultimately articulate better philosophical answers than the merely seemingly impressive answers from ChatGPT. This, in turn, meant students were able to draw upon the philosophical skills and ideas that they had been developing over the semester to collaboratively engage with and challenge this technology. Since it can often be a challenge of teaching philosophy that students are either overly argumentative or still working on developing the confidence to challenge others' ideas, I found that this activity was particularly useful for practicing healthy philosophical collaboration, given that the students would build upon one another's suggestions for what different answers should have incorporated. Meanwhile, those who might be less confident to contribute in class out of concerns about not knowing the "right answer" were emboldened to speak up, since the students were already familiar with some ways of answering the questions. Additionally, I think some students' hesitation to critically engage with philosophical ideas is out of social concerns-that disagreement itself is unkind or likely to hurt another's feelings. Similarly, some (particulaly introductory) students can be overly deferential towards historical philosophers, which contributes to students' hesitation to engage critically with those philosophers' ideas. However, since ChatGPT is neither a "famous philosopher" nor another person in the class, the corresponding hesitations are not present either, which further enables students to practice engaging critically with more confidence.

Ultimately, technological innovations often disrupt social practices, which forces us to reflect on the fundamental values underpinning those practices. The fact that ChatGPT has disrupted practices in education provides an opportunity for us to really reflect on the purposes of our assignments and how best to fulfill those goals. As mentioned, there are (surely imperfect)

technologies that may directly address concerns about new forms of plagiarism and there may be ways to avoid students' opportunities for abusing this technology, but this is also an opportunity to use this technology to innovate in our classrooms as well. For philosophy in particular, this seems like an opportunity for philosophy instructors to gain traction in demonstrating the value of studying philosophy in virtue of ChatGPT's limitations; it is that much more salient how philosophy cannot be outsourced to technology.

Feel free to share in the comments how you plan to respond to ChatGPT in your classes!

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Incorporating ChatGPT in Philosophy Classes - PLATO - Philosophy Learning and Teaching Organization

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Incorporating ChatGPT in Philosophy Classes By Cassie Finley, PhD Candidate in Philosophy, University of Iowa Since the launch of ChatGPT on November 30th, there has been widespread discussion among educators about what this new technology means for education.

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Alarmed by A.I. Chatbots, Universities Start Revamping How They Teach

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With the rise of the popular new chatbot ChatGPT, colleges are restructuring some courses and taking preventive measures.

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I'm never sure how much overlap there is for the various audiences that consist of the John Warner Writer Experience Universe, but while to folks here I am, "The Biblioracle," book recommender par excellence, to a whole other group I am the author of Why They Can't Write: Killing the Five-Paragraph Essay and Other Necessities, and The Writer's Practice: Building Confidence in Your Nonfiction Writing, and a blogger about education issues at Inside Higher Ed.

Modesty aside, and somewhat to my surprise, I have become an expert in how we teach writing. That expertise was birthed from the frustration I experienced in trying to teach writing to first-year college students over the years, and finding them increasingly disoriented by what I was asking them to do, as though there was no continuity between what they'd experienced prior to college, and what was expected of them in college.

These were well-above average students at selective schools (University of Illinois, Virginia Tech, Clemson), who did not necessarily lack writing skill, but had very negative attitudes towards writing. To cut to the chase, and to keep from repeating everything I cover in Why They Can't Write, rather than having students wrestle with the demands of trying to express themselves inside a genuine rhetorical situation (message/audience/purpose), they were instead producing writing-related simulations, utilizing prescriptive rules and templates (like the five-paragraph essay format), which passed muster on standardized tests, but did not prepare them for the demands of writing in college contexts.

My books are a call to change how we approach teaching writing at both a systemic and pedagogical level. What teachers and schools ask students to do is not great, but that asking is bound up with the systems in which it happens, where teachers have too many students, or where grades or the score on an AP test are more important than actually learning stuff. It's not just that we need to change how and what we teach. We have to fundamentally alter the spaces in which this teaching happens.

It is difficult to overstate how bad things have been for a couple of generations of students. This tweet from the writer Lauren Groff ( lamenting what school had done to her son's attitudes towards writing is a not uncommon testimony I hear from parents and students alike.

Deep down, this is a question about what we value, in what we read, what we write, and unfortunately, we have attached a set of values to student writing that are disconnected from anything we actually value about what we read, and what we write.

Along with many others, I've been shouting about these problems for years, often into what felt like a void, but this past week, once people had a chance to see what the ChatGPT could produce, suddenly attention was being paid.

This snapshot from the ChatGPT homepage is a good overview of what OpenAI says ChatGPT can do.

I first wrote about the underlying algorithm (GPT3) at Inside Higher Ed back in March 2021, when it was put to the task of trying to answer some college-level writing assignments and then compared to the output of human students answering the same questions.

For three of the four subjects - research methods, U.S. history and law - the AI passed with a C or better grade. The top human scored better than the AI in each category, but in two categories (research methods, law), the AI beat one or more of the humans.

The subject it tanked was creative writing.

It would not tank creative writing anymore.

At the top next to the purple icon is my prompt. Below that is ChatGPT's response. It took about 60 seconds for it to process and produce the text.

This is not good, exactly, but it also isn't bad. It's perfectly okay, not unlike the kind of thing that a student would produce in an introductory creative writing course. For a short story, that opening sentence is weird, but a clever student making a quick edit could excise it and improve the outcome immediately. In fact, a good overall editorial strategy would be to cut any line that's explicitly in my prompt, so what remains is learned via inference. The weirdest part is perhaps the choice of song, as the upbeat spirit of the song and lyrics about a woman telling her partner not to be tempted by other girls doesn't quite fit the scenario. It's a good example of how the algorithm has no appreciation of meaning and context.

The passage also lacks a sense of place and atmosphere. The descriptions are sterile. I'm going to fix that with an additional prompt.

That's actually starting to approach not bad. It's not good, exactly, but in an introductory class, you don't necessarily expect good. Still, it's lacking something. It feels rather generic, voiceless. Let's see if I can help it out a bit:

This doesn't actually sound like Molly Ivins writing about politics, but the prompt is enough to inject some spark of life into the prose. You can see a couple of immediate improvements over the previous version.

Compare these two sentences:

Eight grade student, Sarah sat on her bed in her room, staring at the ceiling as she thought about Billy, the cute boy in her class.

And

Eighth-grader Sara was holed up in her room, trying to puzzle out the meaning of life, or at least the meaning of Billy, the cute boy in her math class.

The latter is clearly better, more alive. "Holed up" and "puzzled out" are common phrasings, but clearly better than the generic language of the first example. And that line about puzzling out the "meaning of Billy" is downright good.

You know who agrees with me about which is the better narrative? ChatGPT.

If the AI can produce this, imagine what it can do with a canned prompt that you might commonly find in a literature-based high school English class, such as this one, from a sample AP Literature exam, "In many works of fiction, houses take on symbolic importance. Such houses may be literal houses or unconventional houses (e.g., hotels, hospitals, monasteries, or boats). Choose a work of fiction in which a literal or unconventional house serves as a significant symbol. Then, in a well-written essay, analyze how this house contributes to the interpretation of the work as a whole. Do not merely summarize the plot."

I won't bore you by pasting the answer, because it's not worth your time to read it, but it would easily receive the maximum score a 5, particularly given the fact that the AP graders do not pay any attention to whether or not the content of the answer is accurate or true.

I cannot emphasize this enough: ChatGPT is not generating meaning. It is arranging word patterns. I could tell GPT to add in an anomaly for the 1970s - like the girl looking at Billy's Instagram - and it would introduce it into the text without a comment about being anomalous. It is not entirely unlike the old saw about a million monkeys banging on a typewriter for along enough, that one of them would produce the works of Shakespeare through random chance, except this difference is, ChatGPT has been trained on a data set that eliminates all the gibberish.

I sincerely hope that this is the end of the high school English courses that the lamentations are describing because these courses deserve to die, because we can do better than these courses if the actual objective of the courses is to help students learn to write.

But now that this technology is in the world, and will be widely available, we must think about what high school English should look like going forward. I've been thinking about these things for years, so I have a head start on others, but let me be clear ChatGPT has not created a problem that wasn't already present.

One of the assumptions those who say this is the end of high school English make about students is that if students can find an end around doing the actual work of school, they will definitely take it.

What does it say about what we ask students to do in school that we assume they will do whatever they can to avoid it?

The final epiphany that cemented how wrong a turn we had made was the first time I stood in front of a class of first-year students on the second day of our writing course and I presented a hypothetical where I give them all A grades, but class would never meet, they would no no assignments, they would get no feedback or instruction. They would learn nothing. That first time I did it, about 60-65% of students said they'd take that deal.

Disturbing.

The last time I did it, six or seven years later, 85% said they'd take that deal.

Disastrous.

The students were not lazy or entitled. They were responding rationally to the incentives of the system. An A without learning anything was far more valuable than learning anything, and risking a grade lower than an A. School had nothing to do with learning, and writing courses especially were unlikely to be interesting or engaging.

Well, I set out to prove students wrong, that there was writing worth doing, and over the years developed the curriculum that is collected in The Writer's Practicein order to try to prove that.

The experiences in the book are designed to be both intrinsically interesting, and to engender learning that is both visible to students and relevant to their lives. In my experience, students actually do want to learn stuff because students are people too. The problem is school has ceased to honor their humanity, instead substituting "student-ness."

First step, et's give students something worth doing.

It is a mistake to think we can out-prompt the AI. There are some things the AI is not good at yet - for example, it is an inferior lyricist to Steve Miller (not exactly known for the quality of his lyrics) when provided all the rhyming words from "The Joker."

But for the typical school assignment, it reliably turns out passable results.

I've tested some of the core prompts that I use in The Writer's Practice. In isolation, some are vulnerable to ChatGPT, while others would be very difficult for it to pull off convincingly at this time, but based on the progress I've seen, I have little confidence that we can stay ahead of its ability to bullshit.

But the reason why I'm confident my pedagogy is not vulnerable to ChatGPT is because I do not only grade the end product, but instead, value the process it takes to get there. I ask students to describe how and why they did certain things. I collect the work product that precedes the final document.

I talk to the students, one-on-one about themselves, about their work.

If we assume students want to learn - and I do - we should show our interest in their learning, rather than their performance.

Unfortunately, for the vast majority of my career, I did not have the time or resources necessary to fulfill the highest aims of my own pedagogy. The National Council for Teachers of English (NCTE) recommends each instructor teach three sections of a maximum of fifteen students each, for a total of 45 students. I never had fewer than 65 students in a semester, and some semesters had in excess of 150.

High school teachers are working under even greater burdens, and in more challenging circumstances. If the system will not support the teachers who must do the work, we may as well let ourselves be overwhelmed by the algorithm.

The AI does not generate excellent work, particularly not without the kind of tweaking you see me doing above, tweaking which I can do because I possess a relatively sophisticated understanding of narrative craft and know what to tell the AI to do. A student doing an end run around school would likely accept the first thing the bot gives it, turn it in, and hope for the best. It's similar to how plagiarists are easy to catch. They're not diligent enough to cover up their perfidy.

But part of the problem is that we - and very much including myself here - have been conditioned to reward surface-level competence (like fluent prose) with a grade slike a C+, B-, or B. We may have to get used to not rewarding pro forma work that goes through the motions with passing grades, or it may mean finding other elements of the experience to focus on in terms of grading.

This process of "ungrading" or alternative grading has been gaining significant momentum in recent years, and I think it is a promising way of figuring out what is meaningful to students and what kinds of approaches help them learn. For those who are curious about this movement, I recommend Susan Blum's edited collection, Ungrading: Why Rating Students Undermines Learning (And What to Do Instead), but then again, I would because I wrote a chapter in it on Wile E. Coyote as the hero of ungraders.

Having played around with this stuff for only a few days, my thoughts are early and provisional, but I could see potential in crafting assignments that encourage and empower students to utilize the AI in their work. At his Substack,

interviewed the author Chandler Klang Smith, about how she uses a similar technology, Sudowrite, to help produce her fiction. The AI could be used as a tool, or a toy in a way that opens up experiences of learning.

I'm certain there's people who are way ahead of me on how to do this well. I'll be seeking them out and listening to what they have to say.

The reason the appearance of this tech is so shocking is because it forces us to confront what we value, rather than letting the status quo churn along unexamined. I couldn't do that while I was teaching because the way students were struggling made me feel like I was doing a terrible job, and I wanted to figure out how to do better.

It's possible that one of the things we (as in society collectively) will decide is that students don't need to learn to write anymore, since we have technology that can do that for us.

I think this would be a shame because one of the things I value about writing, is the act of writing itself. It is an embodied process that connects me to my own humanity, by putting me in touch with my mind, the same way a vigorous hike through the woods can put me in touch with my body.

For me, writing is simultaneously expression and exploration.

In a piece like this, writing is the expression and exploration of an idea (or collection of ideas). It is only through the writing that I can fully understand what I think.

With fiction, for me, writing is the expression and exploration of for lack of a better word: life. Towards the end of my novel, The Funny Man, my narrator says, "Everyone's got a story, and the best ones are those we tell ourselves." That was a little Easter Egg I put in the text before I had any notion that it would one day be published as a reminder of how interesting it was to solve the puzzle of writing a novel. It is in the mouth of my narrator, but the sentiment is mine.

Writing is rewarding. Writing is empowering. Writing is even fun. As human, we are wired to communicate. We are also wired for "play." Under the right circumstances, writing allows us to do both of these things at the same time.

It is not an impossible challenge to make this universe of experience accessible to students. We know much about what has to be done already.

It's really a matter of whether or not we will decide to do it.

Since I first published this newsletter, I've been working on a course of instructors who assign writing that is designed to help them do their work in a world where ChatGPT exists. More info on the course and how to access it is available at this link right here.
Teaching Writing in an AI World
If you're curious and want to read more reactions to ChatGPT, I recommend this piece from Ian Bogost on how we should see it a "a toy, not a tool." The New York Times podcast "Hard Fork" asks, "Can ChatGPT make this podcast?"This piece by Stephen Johnson is a good one if you want to know more about how ChatGPT does what it does. Writing at Slate, Alex Kantrowitz says that Google has a chat bot that's even better than ChatGPT, but releasing it disrupts their own search business model.

which I wrote about in an earlier newsletter when I asked "How do you know if a book is true?" Also included is Foreverland: On the Divine Tedium of Marriage by

which I swear every married (or unmarried) person should read as a route to improving their own perspective on matrimony.

Here's a quick, music-themed quiz from the Times. I only got four out of five on this one, breaking my perfect streak on these quizzes.

Donna Barba Higuera, winner of this year's Newberry Medal for children's literature for The Last Cuentista, provides a list of her five favorite children's books of 2022.

CrimeReads has released their list of best crime novels of 2022, while the Times has their take on the b est true crime books of the year.

Brittle Paper introduces us to 100 notable African books from this year.

Affiliate income is at $321.30 for the year. We've already topped last year's total of $308.10, but what the heck, why not get to $400 before the year is out. As a reminder, I'm matching up to $500 of affiliate income.

It's gone really well, as far as I'm concerned, but I was recently provided some data about how much more I would be earning if I put at least some of the content behind a paywall, and well...it is quite a bit more.

At the same time, putting posts behind a paywall would reduce the size of my readership, and my primary motive behind maintaining this space is to be read by a community of people interested in some of the same things I'm interested in, so I'm not going to put up a paywall, at least not for these weekly main posts.

But if you do like this newsletter and you can spare the subscription money to support it, I will be incredibly grateful, and the rest of the community thanks you as well.

Alrighty, time to go have a Sunday. You probably won't be hearing from me next week because I'm traveling home to see family and I want to spend time with them, rather than tap tapping on the computer machine, but I have a new installment of "A Book I Wish More People Knew About" that will be coming your way.

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ChatGPT Can't Kill Anything Worth Preserving

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If an algorithm is the death of high school English, maybe that's an okay thing.

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From Alex Podchaski, former Tech Director at Oak Knoll

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Go to Chat.openai.com and create an account to see what all the fuss is about. Otherwise, these resources frame the discussion and concepts around the service.

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From the Berkshire School in MA

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Artificial Intelligence in Education

The resources here were curated with the intent of education around AI and its impact on education. The site does not endorse any of the authors or viewpoints expressed, but rather is trying to give a comprehensive look at the tools, ethics and discussions being had around the topic of AI.

The graphic below shows some of the many tools that are using AI and the myriad of implications it has.

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Artificial Intelligence in Education The resources here were curated with the intent of education around AI and its impact on education. The site does not endorse any of the authors or viewpoints expressed, but rather is trying to give a comprehensive look at the tools, ethics and discussions being had around the topic of AI.

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ISTE offers free resources and guides for educators who are interested in learning about how artificial intelligence can empower learners at every level.

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Join Middle States President Christian Talbot for a live Interview and Q&A with Jennifer Carolan, Co-Founder and Partner at Reach Capital. Carolan and Talbot will discuss Artificial Intelligence, including the arrival of ChatGPT, and what it means for the future of education.

Free Registration: https://docs.google.com/forms/d/e/1FAIpQLSeb3ZfI6e4dFxhq5x55i1-eHYCbxRgYjhprX--ySzPxGuRo-w/viewform

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Virtual Round Table -AI: Artificial Intelligence and Academic Integrity

For: All Educators

Thursday, January 26, 2023

10:00 a.m. - 11:00 a.m.

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Annie ChechitelliChief Product Officer, Turnitin

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Sneak preview of Turnitin’s AI writing and ChatGPT detection capability

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An introduction to our AI Innovation Lab to give you a first-hand glimpse of what our technology (in development) can do​

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Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen
Engineering, Design, Product, and Prototyping
Jeff Belgum, Dave Cummings, Jonathan Gordon, Chris Hallacy, Shawn Jain, Joanne Jang, Fraser Kelton, Vishal Kuo, Joel Lehman, Rachel Lim, Bianca Martin, Evan Morikawa, Rajeev Nayak, Glenn Powell, Krijn Rijshouwer, David Schnurr, Maddie Simens, Kenneth Stanley, Felipe Such, Chelsea Voss, Justin Jay Wang
Comms, Policy, Legal, Ops, Safety, and Security
Steven Adler, Lama Ahmad, Miles Brundage, Kevin Button, Che Chang, Fotis Chantzis, Derek Chen, Frances Choi, Steve Dowling, Elie Georges, Shino Jomoto, Aris Konstantinidis, Gretchen Krueger, Andrew Mayne, Pamela Mishkin, Bob Rotsted, Natalie Summers, Dave Willner, Hannah Wong
Acknowledgments
Thanks to those who helped with and provided feedback on this release: Sandhini Agarwal, Sam Altman, Chester Cho, Peter Hoeschele, Jacob Jackson, Jong Wook Kim, Matt Knight, Jason Kwon, Anna Makanju, Katie Mayer, Bob McGrew, Luke Miller, Mira Murati, Adam Nace, Hyeonwoo Noh, Cullen O'Keefe, Long Ouyang, Michael Petrov, Henrique Ponde de Oliveira Pinto, Alec Radford, Girish Sastry, Pranav Shyam, Aravind Srinivas, Ilya Sutskever, Preston Tuggle, Arun Vijayvergiya, Peter Welinder

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DALL·E 2 is an AI system that can create realistic images and art from a description in natural language.

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The Copyleaks AI Content Detector helps you know if what you're reading was written by a person or was generated by AI, including ChatGPT.

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Princeton student's detection tool

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Edward Tian was fast asleep when his bot broke a website.

The 22-year-old senior at Princeton spent his winter break in his local coffee shop creating GPTZero, an app that he claimed would be able to "quickly and efficiently" tell if an essay was written by a human or by OpenAI's ChatGPT. When he uploaded it to the app creating and hosting platform Streamlit, he didn't expect it to get that much attention.

"I was expecting, at most, a few dozen people trying out the app," Tian told The Daily Beast. "Suddenly, it was crazy in usership with over 2000 people signing up for the beta in a few hours."

GPTZero eventually saw such a massive influx of users that it even crashed the platform that was hosting it. "I'm awestruck that it blew up and went so viral," he added.

When OpenAI released ChatGPT on Nov. 30, 2022, it unleashed a digital Pandora's Box on the world.

Everyone-from high school teachers, to college professors, to journalists-all feared the powerful AI chatbot ushered in a new era of bot-generated essays and articles that some have dubbed " AIgiarism." Some educators have already begun reporting instances of their students using ChatGPT in order to create essays out of whole cloth and finish writing assignments.

While OpenAI has said that they eventually plan on implementing "watermarks" in order to verify whether or not something was created by ChatGPT, there's still no official method of doing so-which can create a giant bot-sized headache across all sectors like education and journalism.

Tian, who's pursuing a double major in computer science and journalism, was bothered by ethical dilemmas posed by chatbots as well as what he described as the "black box" nature of large language models like ChatGPT. The opaque nature of the models results in people fundamentally misunderstanding and, therefore, misusing them.

- Edward Tian, Princeton University

So, even though he is on the cusp of graduating, he decided to spend his winter break building a tool that could help people find out whether or not a piece of writing was likely written by a bot.

"Humans deserve to know when the writing isn't human," Tian sid. "There's so much hype around ChatGPT and AI generation lately, that humans deserve to know the truth."

GPTZero uses two different metrics to assess whether or not a text has been penned by a bot: perplexity, and burstiness. Texts placed into the app will be assigned a number for both metrics. If the number is low, the likelihood of it being created by a bot is higher.

Perplexity is a measurement of randomness in a sentence. If a sentence is constructed or uses words in a way that surprises the app, then it will score higher in perplexity. Tian said that he used the free and open source GPT-2 to help train his app for this metric.

Burstiness is the quality of overall randomness for all the sentences in a text. For example, human writing tends to have sentences that vary in complexity. Some are simple. Some can give James Joyce a run for his money. Bots, on the other hand, tend to generate sentences that are relatively low in complexity, throughout the entire text.

"There are beautiful qualities of human written prose that computers can and should never co-opt," Tian explained. As a journalism student, he was inspired by a class he took with American writer John McPhee who taught him about those beautiful qualities of human writing.

Tian would go on to use an essay by McPhee in The New Yorker as part of his demo for GPTZero:

Despite building the tool, Tian isn't anti-AI. He believes that there's a time and a place for them if used ethically and with consent. Hell, he's even used AI programs like CoPilot to "support much of my coding."

"I'm not opposed to using AI for writing when it makes sense," he said. "But humans deserve to know when the writing isn't human. There's so much hype around ChatGPT and AI generation lately, that humans deserve to know the truth."

With the hype and fears surrounding ChatGPT, a tool like Tian's could prove to be incredibly useful across sectors from educators who want to see if their student plagiarized an essay, to job recruiters who want to check if a cover letter was actually written by an applicant. As such, it could also be incredibly lucrative to the right investors-some of whom have already reached out to Tian with dollar signs in their eyes.

"Just in the past day, a bunch of VCs have slid in my Twitter DMs," Tian said, including the likes of A16Z, Menlo Ventures, and Red Swan. But, he's not done with GPTZero quite yet. He wants to further refine and develop the app, and he even has plans to expand its transparency with "explainers and detection methodologies."

And, at the end of the day, he's a senior in college. He has finals looming, with homework and human-generated essays to worry about. Right now, that's a much bigger concern than a digital Pandora's Box or VC investors

"I'm going to take all the calls but for now," he said with a laugh. "But I'm just a college student focused on graduating from school.

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A College Kid Built an App That Sniffs Out Text Penned by AI

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Edward Tian was fast asleep when his bot broke a website. The 22-year-old senior at Princeton spent his winter break in his local coffee shop creating GPTZero, an app that he claimed would be able to "quickly and efficiently" tell if an essay was written by a human or by OpenAI's ChatGPT.

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Huggyface

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This is an online demo of the GPT-2 output detector model, based on the🤗/Transformers implementation of RoBERTa. Enter some text in the text box; the predicted probabilities will be displayed below. The results start to get reliable after around 50 tokens.

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This is an online demo of the GPT-2 output detector model, based on the🤗/Transformers implementation of RoBERTa. Enter some text in the text box; the predicted probabilities will be displayed below. The results start to get reliable after around 50 tokens.

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Freemium

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Chatsonic a conversational AI chatbot is like ChatGPT but built with superpowers and now GPT-4 powered. Check out the best ChatGPT alternative to create content

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Subscription-based; many students are aware of this tool

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Rytr is an AI writing assistant that helps you create high-quality content, in just a few seconds, at a fraction of the cost!

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21 Pages Posted: 23 Dec 2022

Date Written: December 13, 2022

Abstract

Chatbots are able to produce high-quality, sophisticated text in natural language. The authors of this paper believe that AI can be used to overcome three barriers to learning in the classroom: improving transfer, breaking the illusion of explanatory depth, and training students to critically evaluate explanations. The paper provides background information and techniques on how AI can be used to overcome these barriers and includes prompts and assignments that teachers can incorporate into their teaching. The goal is to help teachers use the capabilities and drawbacks of AI to improve learning

Keywords: AI, education, chatbot, transfer, learning

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new modes of learning enabled by ai chatbots: three methods and assignments

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New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments

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Chatbots are able to produce high-quality, sophisticated text in natural language. The authors of this paper believe that AI can be used to overcome three barri

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(Image credit: Image by Mohamed Hassan from Pixabay )

ChatGPT is the most advanced AI language generator and chatbot ever made widely available to the public. Since November 2022 when ChatGPT went live to the public, more than a million people have used the free tool and the website servers have frequently been overloaded during peak times.

For educators, teaching with ChatGPT is something of a double-edged sword. As a writer and writing professor, I'm very conscious of both these edges. I recently wrote about the ways in which (opens in new tab) such as ChatGPT could be used by students to cheat. The technology is good enough that by simply typing in a few quick prompts, a student can use ChatGPT to churn out complete or nearly complete essays and papers in minutes.

On the other hand, teaching with ChatGPT offers educators many opportunities. The technology can be used to help students outline or organize their papers, and at its best, can serve as a powerful, fun-to-use digital tutor that works like an improved version of Google.

The tool, and I admit this somewhat begrudgingly, can also be fun to use. While I remain wary of its potential for abuse, right now it's a modern tech marvel and seeing its response sometimes feels like watching the tech world's equivalent of a magic show.

What Is ChatGPT?

ChatGPT (opens in new tab) was designed by the Microsoft-backed OpenAI, an AI research company. The GPT stands for generative, pre-trained, transformer.

OpenAI previously released GPT-3, which did eye-catching things such as write an (opens in new tab)for The Guardian and was even used by some to (opens in new tab) the speech patterns of deceased loved ones. ChatGPT builds on GPT-3 technology with the ability to answer questions in a more sophisticated manner. In this way, the model works like an improved version of digital assistants such as Siri and Alexa and as a more efficient Google.

Though much like a traditional search engine, what you learn from it isn't always accurate. You can also prompt it to write passages on certain topics in certain styles.

What Are Some Ways to Teach With ChatGPT?
Use ChatGPT as a Digital Tutor
Because of its quick ability to answer questions, ChatGPT is a fun tool for inquiry. Jeremy Howard, an AI researcher, recently (opens in new tab) with The New York Times how his 7-year-old daughter used ChatGPT as a digital tutor, asking it questions and getting solid answers about what trigonometry is good for and where black holes come from as well as why chickens incubate their eggs.

Many students will likely start using the tool in this way whether their teacher tells them to or not, so addressing this use can be a good proactive teaching practice.

When the January semester begins, I plan on reminding my students that right now ChatGPT is a little like a fairy in an old tale--any gift it offers likely comes with a catch. For example, its responses are not always accurate and while its creators note that there are restrictions in place to limit abuse, ChatGPT might still "occasionally produce harmful instructions or biased content."
Use ChatGPT to Provide a Writing Prompt
One of the biggest hurdles for many students (or anyone really) when it comes to writing is just facing the horror of the blank page and getting started. Using ChatGPT as a prompt can be helpful as long as students can resist the temptation to copy and paste what they find.
Use ChatGPT To Teach Writing Styles
Teaching students to recognize different genres and styles of writing is often an important part of writing class. ChatGPT can be a fun way to help with this because you can ask it to emulate the writing style of an author whose work you are reading for class. Then students can then examine in what spots it misses the mark. For instance, when I asked it to describe snow in the style of Ernest Hemingway, it failed to emulate the short and direct sentences for which the author is known.
Discuss the Moral Implication of the Technology
One of the best teaching opportunities with ChatGPT might be to really dig into its ethical implications and potential problems. Having students critically fact-check its responses can be valuable. As can examining the larger dangers of this technology. Why is using ChatGPT to generate school assignments wrong? Is it wrong? These are the types of questions that I suspect students and society as a whole will be grappling with for years to come and it can't hurt for students to get a head start in thinking about them.

How Do You Sign up for a ChatGPT Account?

(opens in new tab) for free access to ChatGPT requires sharing an email address and phone number. It only takes a few minutes to verify your email and phone number, but educators should check with their IT or technology department before encouraging students to do this.

Educators can also avoid most potential issues with student privacy by exploring ChatGPT as a class using only the teacher's account.

What ChatGPT Says about Teaching With ChatGPT?

I was unsure how to finish this article, so I asked ChatGPT, "What are some ways educators can use ChatGPT to teach?" The response was:
ChatGPT is a chatbot developed by OpenAI, and it is not intended to be used as an educational tool. ChatGPT is a large language model trained to generate human-like text based on the input it receives. It can be used to generate creative writing prompts, to have interesting conversations with, or simply to provide entertainment. However, it is not designed to be used for teaching purposes.
Sam Altman CEO of OpenAI also recently cautioned against relying on ChatGPT just yet. "ChatGPT is incredibly limited, but good enough at some things to create a misleading impression of greatness," he (opens in new tab). "It's a mistake to be relying on it for anything important right now. it's a preview of progress; we have lots of work to do on robustness and truthfulness."

Regardless of what ChatGPT or Altman say, I suspect it's a question of "when" not "if" most students will start using ChatGPT or similar technology as part of their schoolwork. In other words, teaching with ChatGPT is something we're probably going to have to do as educators, whether we want to or not, or that is its intended use or not.

Or at least that's the opinion of this very human writer.

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What is ChatGPT and How Can You Teach With It? Tips & Tricks

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(Image credit: Image by Mohamed Hassan from Pixabay ) ChatGPT is the most advanced AI language generator and chatbot ever made widely available to the public. Since November 2022 when ChatGPT went live to the public, more than a million people have used the free tool and the website servers have frequently been overloaded during peak times.

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Don’t Ban ChatGPT in Schools. Teach With It.

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OpenAI’s new chatbot is raising fears of cheating on homework, but its potential as an educational tool outweighs its risks.

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Augmenting the think-pair-share with ChatGPT could be one of the biggest tech-enabled leaps in pedagogy:
* Think
* Pair 
* ChatGPT
* Pair
* Share

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Augmenting the think-pair-share with ChatGPT could be one of the biggest tech-enabled leaps in pedagogy:
* Think
* Pair 
* ChatGPT
* Pair
* Share

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Augmenting the think-pair-share with ChatGPT could be one of the biggest tech-enabled leaps in pedagogy:
* Think
* Pair
* ChatGPT
* Pair
* Share
— Sarah Dillard (@dillardsarah) December 5, 2022

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Augmenting the think-pair-share with ChatGPT could be one of the biggest tech-enabled leaps in pedagogy:
* Think
* Pair 
* ChatGPT
* Pair
* Share

# data[].attributes.attachment_link.metadata.attachment_props.original_text
Augmenting the think-pair-share with ChatGPT could be one of the biggest tech-enabled leaps in pedagogy:
* Think
* Pair 
* ChatGPT
* Pair
* Share

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Augmenting the think-pair-share with ChatGPT could be one of the biggest tech-enabled leaps in pedagogy:
* Think
* Pair 
* ChatGPT
* Pair
* Share

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Including 10 AI-ready assignment and activity ideas.